-- pgmnemo--0.14.2.sql
-- Flat install for pgmnemo 0.14.2 (curation-honesty: reclassify_corpus() must not overwrite curator-owned types)
-- Generated from: pgmnemo--0.14.1.sql + pgmnemo--0.14.1--0.14.2.sql
-- SPDX-License-Identifier: Apache-2.0

-- pgmnemo--0.13.0.sql
-- Flat install for pgmnemo 0.13.0 (Outcome Loop v2: posterior confidence + use-attribution + min_score)
-- Generated from: pgmnemo--0.12.2.sql + pgmnemo--0.12.2--0.13.0.sql
-- SPDX-License-Identifier: Apache-2.0

-- pgmnemo--0.11.1.sql
-- Flat install: pgmnemo 0.11.1
-- Generated from pgmnemo--0.11.0.sql + pgmnemo--0.11.0--0.11.1.sql
-- SPDX-License-Identifier: Apache-2.0
--
-- Install: CREATE EXTENSION pgmnemo VERSION '0.11.1';
-- Upgrade: ALTER EXTENSION pgmnemo UPDATE TO '0.11.1';
--
-- v0.11.1: typed recall on the hot path
--   Mirrors p_content_types TEXT[] DEFAULT NULL from recall_hybrid() (0.11.0)
--   into recall_fast() (now 6 params) and recall_lessons() (now 8 params).
--   NULL → unchanged behavior (full backward compat).
--   Non-NULL → WHERE content_type = ANY(p_content_types) pushdown in each function.
--   Inherits all v0.11.0 changes (typed recall on recall_hybrid).
--   Inherits all v0.10.1 (#87) fixes.

\echo Use "CREATE EXTENSION pgmnemo VERSION '0.11.1'" to load this file. \quit

-- ─────────────────────────────────────────────────────────────────────────────
-- Trigger helper: updated_at
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo._set_updated_at()
RETURNS TRIGGER
LANGUAGE plpgsql
AS $$
BEGIN
    NEW.updated_at = now();
    RETURN NEW;
END;
$$;

-- ─────────────────────────────────────────────────────────────────────────────
-- Trigger helper: provenance gate
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo._enforce_provenance_gate()
RETURNS TRIGGER
LANGUAGE plpgsql
AS $$
DECLARE
    _gate TEXT;
BEGIN
    IF NEW.commit_sha IS NOT NULL OR NEW.artifact_hash IS NOT NULL THEN
        RETURN NEW;
    END IF;

    BEGIN
        _gate := lower(trim(coalesce(current_setting('pgmnemo.gate_strict', TRUE), '')));
        IF _gate = '' THEN
            _gate := 'enforce';
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _gate := 'enforce';
    END;

    CASE _gate
        WHEN 'enforce' THEN
            RAISE EXCEPTION
                'pgmnemo provenance gate [enforce]: INSERT rejected — '
                'commit_sha or artifact_hash is required. '
                'Recommended: supply a provenance field (the write then succeeds in any '
                'mode and keeps provenance). To relax the gate instead: '
                'SET pgmnemo.gate_strict = ''warn'' (accept with an audit warning) '
                'or ''off'' (skip the check entirely). '
                'See docs/SQL_REFERENCE.md "Disabling the provenance gate".';
        WHEN 'warn' THEN
            RAISE WARNING
                'pgmnemo provenance gate [warn]: INSERT accepted without commit_sha or artifact_hash. '
                'Row will be a ghost lesson (verified_at IS NULL) and excluded from recall by default.';
            RETURN NEW;
        ELSE
            RETURN NEW;
    END CASE;
END;
$$;

COMMENT ON FUNCTION pgmnemo._enforce_provenance_gate() IS
    'Provenance gate trigger: rejects INSERTs where both commit_sha and artifact_hash are NULL. '
    'Controlled by GUC pgmnemo.gate_strict (enforce|warn|off, default enforce).';

-- ─────────────────────────────────────────────────────────────────────────────
-- Core table: agent_lesson (all columns from v0.0.1 through v0.3.0)
-- ─────────────────────────────────────────────────────────────────────────────

CREATE TABLE pgmnemo.agent_lesson (
    id               BIGSERIAL    PRIMARY KEY,
    created_at       TIMESTAMPTZ  NOT NULL DEFAULT now(),
    updated_at       TIMESTAMPTZ  NOT NULL DEFAULT now(),

    -- Lesson content
    role             TEXT         NOT NULL,
    project_id       INT,
    topic            TEXT         NOT NULL,
    lesson_text      TEXT         NOT NULL,
    importance       SMALLINT     NOT NULL DEFAULT 3 CHECK (importance BETWEEN 1 AND 5),

    -- Rich metadata
    metadata         JSONB,

    -- Origin (v0.0.1: TEXT; v0.1.4 index added for BIGINT semantics on source_task_id)
    source_run_id    TEXT,

    -- Provenance gate
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,

    -- Full-text search vectors (generated)
    topic_tsv        TSVECTOR GENERATED ALWAYS AS (to_tsvector('simple', coalesce(topic, ''))) STORED,
    lesson_tsv       TSVECTOR GENERATED ALWAYS AS (to_tsvector('simple', coalesce(lesson_text, ''))) STORED,
    full_text        TSVECTOR GENERATED ALWAYS AS (
                         setweight(to_tsvector('simple', coalesce(topic, '')), 'A') ||
                         setweight(to_tsvector('simple', coalesce(lesson_text, '')), 'B')
                     ) STORED,

    -- Dense vector embedding (pgvector, 1024-dim)
    embedding        vector(1024),

    -- Soft-delete
    is_active        BOOLEAN      NOT NULL DEFAULT TRUE,
    resolved_at      TIMESTAMPTZ,

    -- v0.1.3: verifier provenance
    verifier_role    TEXT,

    -- v0.1.4: lifecycle state machine
    state            TEXT         NOT NULL DEFAULT 'draft'
                         CHECK (state IN ('draft','candidate','validated','canonical',
                                          'deprecated','superseded','archived','rejected','conflicted')),
    state_changed_at TIMESTAMPTZ  NOT NULL DEFAULT NOW(),

    -- v0.1.4: external-system FK (soft references, no REFERENCES constraint)
    source_task_id   BIGINT,

    -- v0.1.4: TTL
    expires_at       TIMESTAMPTZ,

    -- v0.9.0: multimodal schema (#3)
    content_type     TEXT,
    blob_ref         TEXT,
    doc_ref          TEXT,

    -- v0.9.5: recall-recency signals (RFC-PGM-CURATE-260619)
    last_recalled_at TIMESTAMPTZ  DEFAULT NULL,
    recall_count     BIGINT        NOT NULL DEFAULT 0,

    -- v0.9.6: versioned skill items + DAG-scoped recall (R11/R12/R13)
    item_kind        TEXT         NOT NULL DEFAULT 'note'
                         CHECK (item_kind IN ('note','skill_md','template','script','reference','config','spec')),
    version_n        INT          NOT NULL DEFAULT 1,
    patch_count      INT          NOT NULL DEFAULT 0,
    source_dag_id    TEXT         NULL
);

COMMENT ON TABLE pgmnemo.agent_lesson IS
    'Durable, provenance-gated agent lessons. '
    'Rows with verified_at IS NULL are ghost lessons excluded from recall_lessons() by default. '
    'Provenance gate enforces commit_sha OR artifact_hash presence on INSERT.';

COMMENT ON COLUMN pgmnemo.agent_lesson.metadata IS
    'Arbitrary structured context: tags, source agent config, model version, run metadata, etc.';
COMMENT ON COLUMN pgmnemo.agent_lesson.commit_sha IS
    'Git commit SHA that generated or justified this lesson.';
COMMENT ON COLUMN pgmnemo.agent_lesson.artifact_hash IS
    'SHA-256 hex of an external artifact when no git commit applies.';
COMMENT ON COLUMN pgmnemo.agent_lesson.verified_at IS
    'Timestamp at which commit_sha or artifact_hash was successfully verified. '
    'NULL = ghost lesson, excluded from recall by default.';
COMMENT ON COLUMN pgmnemo.agent_lesson.full_text IS
    'Weighted tsvector: topic (weight A) || lesson_text (weight B). '
    'Used by recall_lessons() for full-text scoring component.';
COMMENT ON COLUMN pgmnemo.agent_lesson.embedding IS
    '1024-dim dense vector embedding for semantic recall via pgvector cosine similarity.';
COMMENT ON COLUMN pgmnemo.agent_lesson.verifier_role IS
    'Role that verified the lesson (e.g. PI, automated, founder, peer). NULL = unverified or unknown.';
COMMENT ON COLUMN pgmnemo.agent_lesson.source_run_id IS
    'External run identifier (text); soft reference, no FK constraint.';
COMMENT ON COLUMN pgmnemo.agent_lesson.source_task_id IS
    'External-system FK; not REFERENCES-constrained (allows extension to be portable across host schemas).';
COMMENT ON COLUMN pgmnemo.agent_lesson.expires_at IS
    'Optional hard expiry. NULL = never expires. Rows with expires_at < NOW() are stale.';
COMMENT ON COLUMN pgmnemo.agent_lesson.last_recalled_at IS
    'Timestamp of the most recent recall across all recall paths '
    '(recall_hybrid, recall_lessons, navigate_locate, navigate_expand). '
    'NULL = never recalled since v0.9.5 column addition. '
    'Stamped automatically when pgmnemo.track_recall_recency = on (default). '
    'Primary input to pgmnemo.mark_stale() usage-based curation.';
COMMENT ON COLUMN pgmnemo.agent_lesson.recall_count IS
    'Cumulative recall count since v0.9.5 column addition. '
    'Monotonically increasing; never decremented. '
    'Stamped by the same recall paths as last_recalled_at.';

COMMENT ON COLUMN pgmnemo.agent_lesson.item_kind IS
    'Content category: note (free-form), skill_md (structured skill document), template, '
    'script (executable), reference (external doc link), config, spec. '
    'Default: note. CHECK constraint enforces the allowed set.';

COMMENT ON COLUMN pgmnemo.agent_lesson.version_n IS
    'Monotonically increasing version counter for this lesson. '
    'Incremented when lesson_text is substantially revised. Default 1.';

COMMENT ON COLUMN pgmnemo.agent_lesson.patch_count IS
    'Number of minor patch edits applied since the last version bump. '
    'Reset to 0 on each version_n increment. Default 0.';

COMMENT ON COLUMN pgmnemo.agent_lesson.source_dag_id IS
    'Opaque identifier of the workflow or pipeline run that produced this lesson. '
    'NULL = no workflow origin (manually ingested or from unknown source). '
    'Sparse index ix_pgmnemo_agent_lesson_source_dag_id covers non-NULL rows. '
    'Used by exclude_dag_id parameter in recall_hybrid() and recall_lessons() '
    'to suppress lessons originating from the calling workflow.';

-- ── Indexes ──────────────────────────────────────────────────────────────────

CREATE INDEX pgmnemo_agent_lesson_metadata_idx
    ON pgmnemo.agent_lesson USING GIN (metadata)
    WHERE is_active AND metadata IS NOT NULL;

CREATE INDEX pgmnemo_agent_lesson_full_text_idx
    ON pgmnemo.agent_lesson USING GIN (full_text)
    WHERE is_active;

CREATE INDEX pgmnemo_agent_lesson_topic_tsv_idx
    ON pgmnemo.agent_lesson USING GIN (topic_tsv)
    WHERE is_active;

CREATE INDEX pgmnemo_agent_lesson_lesson_tsv_idx
    ON pgmnemo.agent_lesson USING GIN (lesson_tsv)
    WHERE is_active;

CREATE INDEX pgmnemo_agent_lesson_role_proj_time_idx
    ON pgmnemo.agent_lesson (role, project_id, created_at DESC)
    WHERE is_active;

CREATE INDEX pgmnemo_agent_lesson_role_idx
    ON pgmnemo.agent_lesson (role)
    WHERE is_active;

CREATE INDEX pgmnemo_agent_lesson_project_idx
    ON pgmnemo.agent_lesson (project_id)
    WHERE is_active AND project_id IS NOT NULL;

CREATE INDEX pgmnemo_agent_lesson_verified_idx
    ON pgmnemo.agent_lesson (verified_at)
    WHERE is_active AND verified_at IS NOT NULL;

-- HNSW index (upgraded from ivfflat in v0.1.0)
CREATE INDEX pgmnemo_agent_lesson_embedding_idx
    ON pgmnemo.agent_lesson USING hnsw (embedding vector_cosine_ops)
    WITH (m=16, ef_construction=64)
    WHERE is_active AND embedding IS NOT NULL;

-- v0.1.4 indexes for source provenance columns
CREATE INDEX ix_pgmnemo_lesson_source_task
    ON pgmnemo.agent_lesson (source_task_id)
    WHERE source_task_id IS NOT NULL;

-- v0.1.4 TTL index
CREATE INDEX ix_pgmnemo_agent_lesson_expires
    ON pgmnemo.agent_lesson (expires_at)
    WHERE expires_at IS NOT NULL;

-- v0.9.5: recency index for stale-lesson queries
CREATE INDEX IF NOT EXISTS ix_pgmnemo_agent_lesson_last_recalled
    ON pgmnemo.agent_lesson (last_recalled_at)
    WHERE is_active AND last_recalled_at IS NOT NULL;

-- v0.9.6: sparse index for DAG-origin queries and exclude_dag_id filter
CREATE INDEX IF NOT EXISTS ix_pgmnemo_agent_lesson_source_dag_id
    ON pgmnemo.agent_lesson (source_dag_id)
    WHERE source_dag_id IS NOT NULL;

-- ── Triggers ─────────────────────────────────────────────────────────────────

CREATE TRIGGER agent_lesson_updated_at
    BEFORE UPDATE ON pgmnemo.agent_lesson
    FOR EACH ROW EXECUTE FUNCTION pgmnemo._set_updated_at();

CREATE TRIGGER enforce_provenance_gate
    BEFORE INSERT ON pgmnemo.agent_lesson
    FOR EACH ROW EXECUTE FUNCTION pgmnemo._enforce_provenance_gate();

-- ─────────────────────────────────────────────────────────────────────────────
-- v0.1.4: State machine — allowed-transition table
-- ─────────────────────────────────────────────────────────────────────────────

CREATE TABLE pgmnemo.agent_lesson_state_transition (
    from_state TEXT NOT NULL,
    to_state   TEXT NOT NULL,
    PRIMARY KEY (from_state, to_state)
);

COMMENT ON TABLE pgmnemo.agent_lesson_state_transition IS
    'Allowed state transitions for pgmnemo.agent_lesson.state lifecycle.';

INSERT INTO pgmnemo.agent_lesson_state_transition (from_state, to_state) VALUES
    ('draft',      'candidate'),
    ('draft',      'rejected'),
    ('candidate',  'validated'),
    ('candidate',  'rejected'),
    ('candidate',  'conflicted'),
    ('validated',  'canonical'),
    ('validated',  'rejected'),
    ('canonical',  'deprecated'),
    ('canonical',  'superseded'),
    ('canonical',  'archived'),
    ('canonical',  'conflicted'),
    ('deprecated', 'archived'),
    ('deprecated', 'canonical'),
    ('superseded', 'archived'),
    ('conflicted', 'canonical'),
    ('conflicted', 'rejected'),
    ('conflicted', 'archived');

-- ─────────────────────────────────────────────────────────────────────────────
-- v0.3.0: edge_kind ENUM (MAGMA §3 — temporal + entity graph schema)
-- ─────────────────────────────────────────────────────────────────────────────

CREATE TYPE pgmnemo.edge_kind AS ENUM ('semantic', 'temporal', 'causal', 'entity');

COMMENT ON TYPE pgmnemo.edge_kind IS
    'MAGMA §3 top-level edge category. '
    'causal: cause-effect (edge_type: causal, derives_from, contradicts). '
    'temporal: time-ordered co-occurrence (edge_type: temporal). '
    'semantic: meaning/knowledge relation (edge_type: semantic, elaborates, supersedes). '
    'entity: entity co-membership/reference (edge_type: entity).';

-- ─────────────────────────────────────────────────────────────────────────────
-- v0.2.0: mem_edge — directed typed edges between agent_lesson rows
-- v0.3.0: includes edge_kind ENUM column (NOT NULL, no backfill needed for fresh install)
-- ─────────────────────────────────────────────────────────────────────────────

CREATE TABLE pgmnemo.mem_edge (
    id              BIGSERIAL       PRIMARY KEY,
    source_id       BIGINT          NOT NULL
                        REFERENCES pgmnemo.agent_lesson(id) ON DELETE CASCADE,
    target_id       BIGINT          NOT NULL
                        REFERENCES pgmnemo.agent_lesson(id) ON DELETE CASCADE,
    relation_type   TEXT            NOT NULL,

    -- v0.3.0: MAGMA §3 top-level category (required on all fresh inserts)
    edge_kind       pgmnemo.edge_kind NOT NULL,

    valid_from      TIMESTAMPTZ     NOT NULL DEFAULT now(),
    valid_until     TIMESTAMPTZ,

    weight          REAL            NOT NULL DEFAULT 1.0
                        CHECK (weight BETWEEN 0.0 AND 1.0),

    commit_sha      TEXT,
    metadata        JSONB,

    created_at      TIMESTAMPTZ     NOT NULL DEFAULT now(),
    updated_at      TIMESTAMPTZ     NOT NULL DEFAULT now(),

    CONSTRAINT uq_mem_edge UNIQUE (source_id, target_id, relation_type, valid_from),
    CONSTRAINT ck_no_self_loop CHECK (source_id <> target_id)
);

COMMENT ON TABLE pgmnemo.mem_edge IS
    'Multi-graph relations between lessons. '
    'v0.2.0: initial schema. v0.3.0: edge_kind ENUM (MAGMA §3) + per-kind partial indexes.';

COMMENT ON COLUMN pgmnemo.mem_edge.edge_kind IS
    'MAGMA §3 top-level category (semantic|temporal|causal|entity). '
    'Must be set on all new inserts — use the edge_type→edge_kind mapping in the RFC.';

-- ── Indexes on mem_edge ───────────────────────────────────────────────────────

CREATE INDEX pgmnemo_mem_edge_source_type_idx
    ON pgmnemo.mem_edge (source_id, relation_type)
    WHERE valid_until IS NULL;

CREATE INDEX pgmnemo_mem_edge_target_type_idx
    ON pgmnemo.mem_edge (target_id, relation_type)
    WHERE valid_until IS NULL;

CREATE INDEX pgmnemo_mem_edge_valid_range_idx
    ON pgmnemo.mem_edge (valid_from, valid_until);

-- v0.3.0: per-kind partial indexes (traversal-optimised)
CREATE INDEX ix_mem_edge_kind_causal
    ON pgmnemo.mem_edge (source_id, target_id, weight DESC)
    WHERE edge_kind = 'causal';

CREATE INDEX ix_mem_edge_kind_temporal
    ON pgmnemo.mem_edge (source_id, created_at DESC, target_id)
    WHERE edge_kind = 'temporal';

CREATE INDEX ix_mem_edge_kind_semantic
    ON pgmnemo.mem_edge (source_id, weight DESC, target_id)
    WHERE edge_kind = 'semantic';

CREATE INDEX ix_mem_edge_kind_entity
    ON pgmnemo.mem_edge (source_id, target_id)
    WHERE edge_kind = 'entity';

-- v0.3.0: GIN index on metadata for JSONB attribute queries scoped to any edge_kind
CREATE INDEX ix_mem_edge_metadata_gin
    ON pgmnemo.mem_edge USING GIN (metadata)
    WHERE metadata != '{}'::jsonb;

-- v0.3.0: composite index for mixed-kind queries
CREATE INDEX ix_pgmnemo_mem_edge_kind_time
    ON pgmnemo.mem_edge (edge_kind, created_at DESC);

CREATE TRIGGER mem_edge_updated_at
    BEFORE UPDATE ON pgmnemo.mem_edge
    FOR EACH ROW EXECUTE FUNCTION pgmnemo._set_updated_at();

-- ─────────────────────────────────────────────────────────────────────────────
-- v0.9.6: memory_ingest_log — migration batch tracking
-- ─────────────────────────────────────────────────────────────────────────────

CREATE TABLE pgmnemo.memory_ingest_log (
    id            BIGSERIAL    PRIMARY KEY,
    source_origin TEXT         NOT NULL,
    min_id        BIGINT,
    max_id        BIGINT,
    ingested_at   TIMESTAMPTZ  NOT NULL DEFAULT NOW(),
    retired_at    TIMESTAMPTZ  NULL
);

COMMENT ON TABLE pgmnemo.memory_ingest_log IS
    'Migration batch log: tracks ingestion runs from legacy memory tables into pgmnemo.agent_lesson. '
    'Each row records one batch: source schema/table (source_origin), id range (min_id..max_id), '
    'and ingestion timestamp. Set retired_at when the source table is dropped or the window closes. '
    'Operators may DROP this table once the cutover window is complete.';

COMMENT ON COLUMN pgmnemo.memory_ingest_log.source_origin IS
    'Identifier for the source of the batch, e.g. ''mem.item'' or ''legacy.agent_memory''.';
COMMENT ON COLUMN pgmnemo.memory_ingest_log.min_id IS
    'Lowest source-table id ingested in this batch (inclusive). NULL = unknown.';
COMMENT ON COLUMN pgmnemo.memory_ingest_log.max_id IS
    'Highest source-table id ingested in this batch (inclusive). NULL = unknown.';
COMMENT ON COLUMN pgmnemo.memory_ingest_log.ingested_at IS
    'Timestamp when the ingest batch completed.';
COMMENT ON COLUMN pgmnemo.memory_ingest_log.retired_at IS
    'Timestamp when the source was retired / decommissioned. NULL = still active.';

-- ─────────────────────────────────────────────────────────────────────────────
-- Functions
-- ─────────────────────────────────────────────────────────────────────────────

-- version() — always returns live extversion from pg_catalog
CREATE OR REPLACE FUNCTION pgmnemo.version()
RETURNS TEXT
LANGUAGE SQL
STABLE
PARALLEL SAFE
AS $$
    SELECT extversion FROM pg_extension WHERE extname = 'pgmnemo';
$$;

COMMENT ON FUNCTION pgmnemo.version() IS
    'Returns the currently-installed pgmnemo version by querying pg_catalog.pg_extension.';

-- ingest() — validated public write API (v0.1.0)
CREATE OR REPLACE FUNCTION pgmnemo.ingest(
    p_role          TEXT,
    p_project_id    INT,
    p_topic         TEXT,
    p_lesson_text   TEXT,
    p_importance    SMALLINT     DEFAULT 3,
    p_embedding     vector(1024) DEFAULT NULL,
    p_commit_sha    TEXT         DEFAULT NULL,
    p_artifact_hash TEXT         DEFAULT NULL,
    p_metadata      JSONB        DEFAULT '{}'::jsonb
) RETURNS BIGINT
LANGUAGE plpgsql AS $$
DECLARE
    new_id BIGINT;
BEGIN
    IF p_embedding IS NOT NULL AND vector_dims(p_embedding) <> 1024 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: embedding dimension mismatch — expected 1024, got %',
            vector_dims(p_embedding);
    END IF;

    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance, embedding,
        commit_sha, artifact_hash, metadata, verified_at
    ) VALUES (
        p_role, p_project_id, p_topic, p_lesson_text, p_importance, p_embedding,
        p_commit_sha, p_artifact_hash, p_metadata,
        CASE WHEN p_commit_sha IS NOT NULL OR p_artifact_hash IS NOT NULL
             THEN NOW() ELSE NULL END
    ) RETURNING id INTO new_id;

    RETURN new_id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.ingest(TEXT, INT, TEXT, TEXT, SMALLINT, vector, TEXT, TEXT, JSONB) IS
    'Validated public write API. Use this instead of raw INSERT.';

-- recall_lessons() — v0.3.0: BFS uses edge_kind ENUM (MAGMA §3 fix)
CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT     DEFAULT 10,
    role_filter       TEXT    DEFAULT NULL,
    project_id_filter INT     DEFAULT NULL,
    query_text        TEXT    DEFAULT NULL
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE plpgsql
VOLATILE
AS $$
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
BEGIN
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT,
            100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION,
        0.08
    );

    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at,
            CASE
                WHEN al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL OR al.project_id = recall_lessons.project_id_filter)
          AND (al.embedding IS NOT NULL OR _has_text)
    ),
    anchors AS (
        SELECT id
        FROM candidates
        ORDER BY vec_score DESC
        LIMIT 5
    ),
    -- BFS through causal + temporal edges (v0.3.0: uses edge_kind ENUM)
    graph_walk (anchor_id, depth, reached_id) AS (
        SELECT id, 0, id
        FROM anchors

        UNION ALL

        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT
            gw.reached_id                                                          AS lesson_id,
            MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION)  AS proximity
        FROM graph_walk gw
        WHERE gw.depth > 0
        GROUP BY gw.reached_id
    )
    SELECT
        c.id                                                                  AS lesson_id,
        (
            0.4 * c.vec_score
          + 0.2 * (c.importance::DOUBLE PRECISION / 5.0)
          + _gamma * GREATEST(0.0,
                        1.0 - LEAST(
                            EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0),
                            1.0
                        )
                    )::DOUBLE PRECISION
          + 0.1 * (CASE
                     WHEN c.commit_sha IS NOT NULL AND c.verified_at IS NOT NULL THEN 1.0
                     WHEN c.commit_sha IS NOT NULL                               THEN 0.4
                     ELSE                                                             0.0
                   END)::DOUBLE PRECISION
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        )                                                                     AS score,
        c.role,
        c.project_id,
        c.topic,
        c.lesson_text,
        c.importance,
        c.metadata,
        c.commit_sha,
        c.artifact_hash,
        c.verified_at,
        c.created_at
    FROM candidates c
    LEFT JOIN graph_proximity gp ON gp.lesson_id = c.id
    ORDER BY
        (
            0.4 * c.vec_score
          + 0.2 * (c.importance::DOUBLE PRECISION / 5.0)
          + _gamma * GREATEST(0.0,
                        1.0 - LEAST(
                            EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0),
                            1.0
                        )
                    )::DOUBLE PRECISION
          + 0.1 * (CASE
                     WHEN c.commit_sha IS NOT NULL AND c.verified_at IS NOT NULL THEN 1.0
                     WHEN c.commit_sha IS NOT NULL                               THEN 0.4
                     ELSE                                                             0.0
                   END)::DOUBLE PRECISION
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        ) DESC,
        c.importance DESC,
        c.created_at DESC
    LIMIT k;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT) IS
    'Hybrid recall v0.3.0 — formula: '
    '0.4×cosine + 0.2×(importance/5) + γ×recency(90d) + 0.1×prov_strength + δ×graph_proximity. '
    'graph_proximity = MAX(1 - depth/max_depth) over causal+temporal BFS (edge_kind ENUM) '
    'from top-5 cosine anchors (max_depth=5). '
    'v0.3.0: BFS now uses edge_kind IN (''causal'',''temporal'') — fixes v0.2.x relation_type bug. '
    'γ = pgmnemo.recency_weight (default 0.08). '
    'ef_search = pgmnemo.ef_search GUC (default 100, applied via SET LOCAL). '
    'δ = pgmnemo.graph_proximity_weight (default 0.2, range 0.0–0.5).';

-- recall_lessons_pooled() — cross-role recall wrapper (v0.1.2)
CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons_pooled(
    query_embedding  vector(1024),
    k                INT  DEFAULT 10,
    app_id           INT  DEFAULT NULL
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $$
    SELECT * FROM pgmnemo.recall_lessons(query_embedding, k, NULL, app_id, NULL);
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons_pooled(vector, INT, INT) IS
    'Cross-role recall wrapper: calls recall_lessons() with role=NULL (pooled — no role filter).';

-- transition_lesson() — state machine transition (v0.1.4)
CREATE OR REPLACE FUNCTION pgmnemo.transition_lesson(lesson_id BIGINT, new_state TEXT)
RETURNS pgmnemo.agent_lesson
LANGUAGE plpgsql
AS $$
DECLARE
    _lesson pgmnemo.agent_lesson;
BEGIN
    SELECT * INTO _lesson
    FROM pgmnemo.agent_lesson
    WHERE id = lesson_id
    FOR UPDATE;

    IF NOT FOUND THEN
        RAISE EXCEPTION 'lesson % not found', lesson_id;
    END IF;

    IF NOT EXISTS (
        SELECT 1 FROM pgmnemo.agent_lesson_state_transition
        WHERE from_state = _lesson.state AND to_state = new_state
    ) THEN
        RAISE EXCEPTION 'invalid state transition: % → %', _lesson.state, new_state;
    END IF;

    UPDATE pgmnemo.agent_lesson
    SET state            = new_state,
        state_changed_at = NOW()
    WHERE id = lesson_id
    RETURNING * INTO _lesson;

    RETURN _lesson;
END;
$$;

COMMENT ON FUNCTION pgmnemo.transition_lesson(BIGINT, TEXT) IS
    'Advance a lesson to new_state; raises if the transition is not permitted.';

-- evict_expired_lessons() — TTL purge (v0.1.4)
CREATE OR REPLACE FUNCTION pgmnemo.evict_expired_lessons()
RETURNS INT
LANGUAGE plpgsql
AS $$
DECLARE
    evicted INT;
BEGIN
    WITH deleted AS (
        DELETE FROM pgmnemo.agent_lesson
        WHERE expires_at IS NOT NULL
          AND expires_at < NOW()
        RETURNING 1
    )
    SELECT COUNT(*) INTO evicted FROM deleted;
    RETURN COALESCE(evicted, 0);
END;
$$;

COMMENT ON FUNCTION pgmnemo.evict_expired_lessons() IS
    'Deletes all lessons whose expires_at is non-NULL and in the past. '
    'Returns the number of rows removed. Safe to call frequently.';

-- traverse_causal_chain() — v0.3.0: uses edge_kind for causal traversal
CREATE OR REPLACE FUNCTION pgmnemo.traverse_causal_chain(
    start_id        BIGINT,
    max_depth       INT     DEFAULT 5,
    relation_types  TEXT[]  DEFAULT ARRAY['causal', 'derives_from', 'contradicts'],
    only_active     BOOLEAN DEFAULT TRUE,
    direction       TEXT    DEFAULT 'forward'
)
RETURNS TABLE (
    lesson_id       BIGINT,
    depth           INT,
    path            BIGINT[],
    path_weight     REAL,
    role            TEXT,
    topic           TEXT,
    lesson_text     TEXT,
    importance      SMALLINT,
    created_at      TIMESTAMPTZ,
    commit_sha      TEXT,
    verified_at     TIMESTAMPTZ
)
LANGUAGE plpgsql
STABLE
PARALLEL SAFE
AS $$
BEGIN
    IF direction NOT IN ('forward', 'backward', 'both') THEN
        RAISE EXCEPTION
            'pgmnemo.traverse_causal_chain: direction must be ''forward'', ''backward'', or ''both'' — got: %',
            direction;
    END IF;

    RETURN QUERY
    WITH RECURSIVE causal_walk(lesson_id, depth, path, path_weight) AS (
        SELECT
            start_id,
            0,
            ARRAY[start_id],
            1.0::REAL

        UNION ALL

        -- Forward: source → target (causal kind, relation_type in relation_types)
        SELECT
            me.target_id,
            cw.depth + 1,
            cw.path || me.target_id,
            cw.path_weight * COALESCE(me.weight, 1.0)
        FROM causal_walk cw
        JOIN pgmnemo.mem_edge me ON me.source_id = cw.lesson_id
        WHERE direction IN ('forward', 'both')
          AND me.edge_kind = 'causal'
          AND me.relation_type = ANY(relation_types)
          AND cw.depth < max_depth
          AND NOT (me.target_id = ANY(cw.path))

        UNION ALL

        -- Backward: target → source (causal kind, relation_type in relation_types)
        SELECT
            me.source_id,
            cw.depth + 1,
            cw.path || me.source_id,
            cw.path_weight * COALESCE(me.weight, 1.0)
        FROM causal_walk cw
        JOIN pgmnemo.mem_edge me ON me.target_id = cw.lesson_id
        WHERE direction IN ('backward', 'both')
          AND me.edge_kind = 'causal'
          AND me.relation_type = ANY(relation_types)
          AND cw.depth < max_depth
          AND NOT (me.source_id = ANY(cw.path))
    )
    SELECT
        al.id,
        cw.depth,
        cw.path,
        cw.path_weight,
        al.role,
        al.topic,
        al.lesson_text,
        al.importance,
        al.created_at,
        al.commit_sha,
        al.verified_at
    FROM causal_walk cw
    JOIN pgmnemo.agent_lesson al ON al.id = cw.lesson_id
    WHERE cw.depth > 0
      AND (NOT only_active OR al.is_active)
    ORDER BY cw.depth, cw.path_weight DESC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN, TEXT) IS
    'BFS traversal of causal edges in pgmnemo.mem_edge (v0.3.0). '
    'Filters on edge_kind = ''causal'' + relation_type IN relation_types. '
    'Default relation_types: causal, derives_from, contradicts (MAGMA §3). '
    'direction: ''forward'' (source→target), ''backward'' (target→source), ''both''. '
    'Cycle guard via path array.';

-- traverse_temporal_window() — co-temporal episode discovery (v0.2.0)
CREATE OR REPLACE FUNCTION pgmnemo.traverse_temporal_window(
    start_id            BIGINT,
    window_interval     INTERVAL    DEFAULT INTERVAL '15 minutes',
    include_unlinked    BOOLEAN     DEFAULT TRUE,
    role_filter         TEXT        DEFAULT NULL,
    project_id_filter   INT         DEFAULT NULL,
    k                   INT         DEFAULT 20
)
RETURNS TABLE (
    lesson_id       BIGINT,
    time_delta_sec  DOUBLE PRECISION,
    linked          BOOLEAN,
    edge_weight     REAL,
    role            TEXT,
    topic           TEXT,
    lesson_text     TEXT,
    importance      SMALLINT,
    created_at      TIMESTAMPTZ,
    commit_sha      TEXT,
    verified_at     TIMESTAMPTZ
)
LANGUAGE plpgsql
STABLE
PARALLEL SAFE
AS $$
DECLARE
    _start_ts           TIMESTAMPTZ;
    _include_unverified BOOLEAN;
BEGIN
    SELECT al.created_at INTO _start_ts
    FROM pgmnemo.agent_lesson al
    WHERE al.id = start_id;

    IF NOT FOUND THEN
        RETURN;
    END IF;

    _include_unverified := COALESCE(
        (current_setting('pgmnemo.include_unverified', true) = 'on'),
        FALSE
    );

    RETURN QUERY
    WITH candidates AS (
        SELECT
            al.id,
            al.role,
            al.topic,
            al.lesson_text,
            al.importance,
            al.created_at,
            al.commit_sha,
            al.verified_at,
            ABS(EXTRACT(EPOCH FROM (al.created_at - _start_ts))) AS delta_sec
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.id <> start_id
          AND al.created_at BETWEEN (_start_ts - window_interval)
                                AND (_start_ts + window_interval)
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter         IS NULL OR al.role       = role_filter)
          AND (project_id_filter   IS NULL OR al.project_id = project_id_filter)
    ),
    edges AS (
        SELECT e.target_id AS other_id, e.weight
        FROM pgmnemo.mem_edge e
        WHERE e.source_id = start_id
          AND e.valid_until IS NULL
        UNION ALL
        SELECT e.source_id AS other_id, e.weight
        FROM pgmnemo.mem_edge e
        WHERE e.target_id = start_id
          AND e.valid_until IS NULL
    )
    SELECT
        c.id                            AS lesson_id,
        c.delta_sec                     AS time_delta_sec,
        (e.weight IS NOT NULL)          AS linked,
        e.weight                        AS edge_weight,
        c.role,
        c.topic,
        c.lesson_text,
        c.importance,
        c.created_at,
        c.commit_sha,
        c.verified_at
    FROM candidates c
    LEFT JOIN edges e ON e.other_id = c.id
    WHERE include_unlinked OR e.weight IS NOT NULL
    ORDER BY c.delta_sec ASC, c.importance DESC
    LIMIT k;
END;
$$;

COMMENT ON FUNCTION pgmnemo.traverse_temporal_window(BIGINT, INTERVAL, BOOLEAN, TEXT, INT, INT) IS
    'Return up to k agent_lesson rows whose created_at falls within ±window_interval of start_id. '
    'linked=TRUE when a mem_edge (any direction) exists between that row and start_id. '
    'include_unlinked=FALSE restricts output to explicitly connected lessons.';

-- ─────────────────────────────────────────────────────────────────────────────
-- v0.2.1: Row-Level Security (Q5)
-- ─────────────────────────────────────────────────────────────────────────────

DO $$
BEGIN
    PERFORM set_config('pgmnemo.ef_search',   '100', FALSE);
    PERFORM set_config('pgmnemo.recency_weight', '0.08', FALSE);
    PERFORM set_config('pgmnemo.tenant_id',   '', FALSE);
EXCEPTION WHEN OTHERS THEN
    NULL;
END;
$$;

ALTER TABLE pgmnemo.agent_lesson ENABLE ROW LEVEL SECURITY;

CREATE POLICY agent_lesson_tenant_isolation
    ON pgmnemo.agent_lesson
    AS PERMISSIVE
    FOR ALL
    USING (
        COALESCE(current_setting('pgmnemo.tenant_id', TRUE), '') = ''
        OR
        project_id::TEXT = current_setting('pgmnemo.tenant_id', TRUE)
    );

COMMENT ON POLICY agent_lesson_tenant_isolation ON pgmnemo.agent_lesson IS
    'Multi-tenant row isolation by project_id. '
    'SET pgmnemo.tenant_id = ''<id>'' to restrict the session to that project. '
    'Empty or unset tenant_id bypasses the policy (service-account mode).';

ALTER TABLE pgmnemo.mem_edge ENABLE ROW LEVEL SECURITY;

CREATE POLICY mem_edge_tenant_isolation
    ON pgmnemo.mem_edge
    AS PERMISSIVE
    FOR ALL
    USING (
        COALESCE(current_setting('pgmnemo.tenant_id', TRUE), '') = ''
        OR
        EXISTS (
            SELECT 1 FROM pgmnemo.agent_lesson al
            WHERE al.id IN (source_id, target_id)
              AND (
                  COALESCE(current_setting('pgmnemo.tenant_id', TRUE), '') = ''
                  OR al.project_id::TEXT = current_setting('pgmnemo.tenant_id', TRUE)
              )
        )
    );

COMMENT ON POLICY mem_edge_tenant_isolation ON pgmnemo.mem_edge IS
    'Multi-tenant row isolation: visible when source or target lesson belongs to the session tenant. '
    'Empty / unset tenant_id bypasses the policy (service-account mode).';
-- pgmnemo 0.3.1 → 0.4.0 upgrade
--
-- THEME: Hybrid retrieval promoted to default in recall_lessons().
--
-- Bench evidence (real-DB, 2026-05-15):
--   LoCoMo session-level (DRAGON):
--     recall@10  0.7951 → 0.8409  (+4.15pp, p_corr=0.0156, SIGNIFICANT)
--     MRR        0.5569 → 0.6365  (+7.96pp, p_corr<0.0001, SIGNIFICANT)
--     open_domain/MRR: +9.79pp (p_corr=0.0009)
--     5 significant improvements, 0 regressions across 24 cells
--   LongMemEval-S (bge-m3):
--     recall@10  0.9334 → 0.9334  (+0.00pp, neutral — already saturated)
--     MRR        0.8472 → 0.8521  (+0.49pp, neutral)
--
-- Honest scope:
--   ✓ Significant lift on conversational dialog retrieval (LoCoMo paper-canonical)
--   ✓ No regression anywhere
--   ✗ Does NOT close the BM25 gap on LongMemEval (BM25=0.982, pgmnemo=0.9334)
--   ✗ Adopters with bge-m3-strength embeddings on multi-doc retrieval will see
--     no measurable change (which is fine; provenance gate is the real moat)
--
-- Migration design:
--   1. Idempotent install of recall_hybrid() + lesson_tsv prerequisites
--      (these may already exist if v0.2.2 hybrid opt-in was applied — CREATE
--      OR REPLACE / IF NOT EXISTS make re-application safe).
--   2. recall_lessons() rewritten as a thin router:
--        - If query_text present and pgmnemo.disable_hybrid is FALSE/unset:
--          delegate to recall_hybrid() and project to 12-column shape.
--        - Otherwise: original v0.3.0 vector-only body, unchanged.
--   3. Opt-out: SET pgmnemo.disable_hybrid = 'true' restores v0.3.0 behaviour.



-- ─────────────────────────────────────────────────────────────────────────────
-- S1: lesson_tsv column + trigger + GIN index (idempotent)
--     If already present from v0.2.2 hybrid opt-in: no-op.
-- ─────────────────────────────────────────────────────────────────────────────

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS lesson_tsv tsvector;

CREATE OR REPLACE FUNCTION pgmnemo._update_lesson_tsv() RETURNS TRIGGER AS $$
BEGIN
    NEW.lesson_tsv := to_tsvector('english', COALESCE(NEW.lesson_text, ''));
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

DROP TRIGGER IF EXISTS pgmnemo_agent_lesson_tsv_trg ON pgmnemo.agent_lesson;
CREATE TRIGGER pgmnemo_agent_lesson_tsv_trg
    BEFORE INSERT OR UPDATE OF lesson_text
    ON pgmnemo.agent_lesson
    FOR EACH ROW
    EXECUTE FUNCTION pgmnemo._update_lesson_tsv();

-- Backfill any rows with NULL lesson_tsv
UPDATE pgmnemo.agent_lesson
SET lesson_text = lesson_text
WHERE lesson_tsv IS NULL;

CREATE INDEX IF NOT EXISTS pgmnemo_agent_lesson_tsv_gin_idx
    ON pgmnemo.agent_lesson USING GIN (lesson_tsv);


-- ─────────────────────────────────────────────────────────────────────────────
-- S2: recall_hybrid() function (CREATE OR REPLACE — idempotent)
--     Body is identical to v0.2.2 EXPERIMENTAL opt-in.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;   -- F2: ghost guidance
    _fetch_k_vec        INT;   -- #4: bounded HNSW fetch
    _fetch_k_bm25       INT;   -- #4: bounded BM25 fetch
    _conf_boost_w       DOUBLE PRECISION;  -- I1: confidence boost weight
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    -- I1: read confidence boost weight GUC (default 0.0 = OFF, clamped [0.0, 0.01])
    BEGIN
        _conf_boost_w := GREATEST(0.0, LEAST(0.01, COALESCE(
            NULLIF(current_setting('pgmnemo.confidence_boost_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));
    EXCEPTION WHEN OTHERS THEN _conf_boost_w := 0.0;
    END;

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    -- #4 C2 fix: fetch_k floors — HNSW arm respects ef_search, BM25 arm floors at 40
    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    RETURN QUERY
    WITH RECURSIVE
    -- Phase 1: HNSW vector retrieval (index scan)
    vec_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS raw_vec_score,
            0.0::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_vec
          AND al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY al.embedding <=> query_embedding   -- HNSW index scan
        LIMIT _fetch_k_vec                           -- C2: GREATEST(k*4, _ef_search)
    ),
    -- Phase 2: GIN BM25 retrieval (index scan)
    bm25_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            ts_rank_cd(
                setweight(to_tsvector('english', COALESCE(al.topic, '')), 'A') || al.lesson_tsv,
                _tsquery, 32
            )::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_text
          AND al.is_active
          AND (al.lesson_tsv @@ _tsquery
               OR to_tsvector('english', COALESCE(al.topic, '')) @@ _tsquery)
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY raw_bm25_score DESC
        LIMIT _fetch_k_bm25                          -- C2: GREATEST(k*4, 40)
    ),
    -- Merge: LEFT JOIN + anti-join UNION ALL (each id exactly once)
    all_candidates AS (
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(b.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM vec_candidates v
        LEFT JOIN bm25_candidates b ON b.id = v.id

        UNION ALL

        SELECT
            b.id, b.role, b.project_id, b.topic, b.lesson_text,
            b.importance, b.metadata, b.commit_sha, b.artifact_hash,
            b.verified_at, b.created_at, b.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            b.raw_bm25_score
        FROM bm25_candidates b
        WHERE b.id NOT IN (SELECT id FROM vec_candidates)
    ),
    -- RRF ranking over bounded candidate set
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST)   AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
              -- I1: additive zero-centered confidence boost (w=0 when GUC off → no effect)
              + _conf_boost_w * (s.confidence::DOUBLE PRECISION - 0.5)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    )
    SELECT
        f.id                   AS lesson_id,
        f.final_score          AS score,
        f.v_score              AS vec_score,
        f.b_score              AS bm25_score,
        f.rrf_sparse           AS rrf_score,
        f.role,
        f.project_id,
        f.topic,
        f.lesson_text,
        f.importance,
        f.metadata,
        f.commit_sha,
        f.artifact_hash,
        f.verified_at,
        f.created_at,
        f.confidence::REAL,
        LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
    FROM final f
    ORDER BY f.final_score DESC, f.id ASC   -- C7: tie-breaker for determinism
    LIMIT k;

    -- F2: ghost guidance — if 0 rows returned, check for unverified (ghost) lessons in scope
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT) IS
    'v0.9.2 — I1: confidence-weighted ranking (additive, zero-centered). '
    'GUC pgmnemo.confidence_boost_weight (default 0.0 = OFF, range [0.0, 0.01]). '
    'When ON: final_score += w * (confidence - 0.5). Recommended w=0.003. '
    'Cold-start (confidence=0.5) gets zero boost. High-vs-low delta at w=0.003 ≈ 0.0024 ≈ 8-15 RRF positions. '
    'Activation gate: pending positive A/B validation. '
    'v0.8.2 — F2: NOTICE when 0 rows returned and ghost lessons exist in scope. '
    'Two-phase indexed retrieval: HNSW (pgvector) + GIN (BM25) → RRF fusion → graph proximity boost. '
    'match_confidence: vec_score (cosine similarity, [0,1]). On text-only path (NULL embedding) = 0.0. '
    'STABLE PARALLEL SAFE.';


-- ─────────────────────────────────────────────────────────────────────────────
-- S3: recall_lessons() — REWRITTEN as router
--
--     query_text present + hybrid not disabled → delegate to recall_hybrid()
--     otherwise                                → original vector-only body
--
--     Adopters needing strict v0.3.0 behaviour: SET pgmnemo.disable_hybrid='true'
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT     DEFAULT 10,
    role_filter       TEXT    DEFAULT NULL,
    project_id_filter INT     DEFAULT NULL,
    query_text        TEXT    DEFAULT NULL
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE plpgsql
VOLATILE
AS $$
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
BEGIN
    -- v0.4.0: route to recall_hybrid() when query_text present and not disabled
    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _disable_hybrid := FALSE;
    END;

    IF NOT _disable_hybrid
       AND query_text IS NOT NULL
       AND length(trim(query_text)) > 0
       AND query_embedding IS NOT NULL THEN
        RETURN QUERY
        SELECT
            h.lesson_id,
            h.score,
            h.role,
            h.project_id,
            h.topic,
            h.lesson_text,
            h.importance,
            h.metadata,
            h.commit_sha,
            h.artifact_hash,
            h.verified_at,
            h.created_at
        FROM pgmnemo.recall_hybrid(
            query_embedding,
            query_text,
            k,
            role_filter,
            project_id_filter,
            0.4,    -- vec_weight
            0.4,    -- bm25_weight
            60      -- rrf_k
        ) h;
        RETURN;
    END IF;

    -- Vector-only path: unchanged from v0.3.0 body
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT,
            100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION,
        0.08
    );

    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at,
            CASE
                WHEN al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL OR al.project_id = recall_lessons.project_id_filter)
          AND (al.embedding IS NOT NULL OR _has_text)
    ),
    anchors AS (
        SELECT id
        FROM candidates
        ORDER BY vec_score DESC
        LIMIT 5
    ),
    -- BFS through causal + temporal edges (v0.3.0: uses edge_kind ENUM)
    graph_walk (anchor_id, depth, reached_id) AS (
        SELECT id, 0, id
        FROM anchors

        UNION ALL

        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT
            gw.reached_id                                                          AS lesson_id,
            MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION)  AS proximity
        FROM graph_walk gw
        WHERE gw.depth > 0
        GROUP BY gw.reached_id
    )
    SELECT
        c.id                                                                  AS lesson_id,
        (
            0.4 * c.vec_score
          + 0.2 * (c.importance::DOUBLE PRECISION / 5.0)
          + _gamma * GREATEST(0.0,
                        1.0 - LEAST(
                            EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0),
                            1.0
                        )
                    )::DOUBLE PRECISION
          + 0.1 * (CASE
                     WHEN c.commit_sha IS NOT NULL AND c.verified_at IS NOT NULL THEN 1.0
                     WHEN c.commit_sha IS NOT NULL                               THEN 0.4
                     ELSE                                                             0.0
                   END)::DOUBLE PRECISION
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        )                                                                     AS score,
        c.role,
        c.project_id,
        c.topic,
        c.lesson_text,
        c.importance,
        c.metadata,
        c.commit_sha,
        c.artifact_hash,
        c.verified_at,
        c.created_at
    FROM candidates c
    LEFT JOIN graph_proximity gp ON gp.lesson_id = c.id
    ORDER BY
        (
            0.4 * c.vec_score
          + 0.2 * (c.importance::DOUBLE PRECISION / 5.0)
          + _gamma * GREATEST(0.0,
                        1.0 - LEAST(
                            EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0),
                            1.0
                        )
                    )::DOUBLE PRECISION
          + 0.1 * (CASE
                     WHEN c.commit_sha IS NOT NULL AND c.verified_at IS NOT NULL THEN 1.0
                     WHEN c.commit_sha IS NOT NULL                               THEN 0.4
                     ELSE                                                             0.0
                   END)::DOUBLE PRECISION
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        ) DESC,
        c.importance DESC,
        c.created_at DESC
    LIMIT k;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT) IS
    'Hybrid retrieval router v0.4.0. '
    'When query_text is non-empty and embedding is present (and disable_hybrid GUC '
    'is FALSE/unset), delegates to recall_hybrid() with default weights '
    '(vec_weight=0.4, bm25_weight=0.4, rrf_k=60). Otherwise uses v0.3.0 vector-only '
    'body unchanged. SET pgmnemo.disable_hybrid = ''true'' restores strict '
    'vector-only behaviour.';

-- Bench evidence summary stored as table comment for adopter visibility
COMMENT ON COLUMN pgmnemo.agent_lesson.lesson_tsv IS
    'tsvector populated by pgmnemo_agent_lesson_tsv_trg trigger. '
    'GIN-indexed for BM25 retrieval via ts_rank_cd in recall_hybrid(). '
    'Required as of v0.4.0 (was opt-in in v0.2.2).';
-- pgmnemo 0.4.0 → 0.4.1 upgrade
--
-- THEME: Production hardening per first external
--        production-user feedback). Operational observability + safe deprecation.
--
-- Bench evidence (real-DB, expected 2026-05-21 after Phase 2):
--   LoCoMo session    : neutral expected — R1 default change 0.08 → 0.05 may
--                       cause near-threshold drift; OVERALL r@10 = 0.8409 hold
--   LoCoMo segment    : neutral expected — router unchanged
--   LongMemEval-S     : neutral expected — hybrid neutral on bge-m3 saturated
--   Prod corpus       : recall@10 gate must hold after
--                       default change (harness rerun post-ship)
--
-- Honest scope:
--   ✓ pgmnemo.stats() one-query health check (R3)
--   ✓ vec_score / bm25_score / rrf_score in recall_lessons output (R4)
--   ✓ recency_weight default 0.08 → 0.05 per an internal ablation (R1 code part)
--   ✓ orphan_count signal in pgmnemo.stats() (R7)
--   ✓ traverse_causal_chain 4-arg overload restored with RAISE NOTICE (R10)
--   ✗ Recall algorithm itself unchanged — same router as v0.4.0
--   ✗ No new graph capabilities — mem_edge contract docs shipped 2026-05-16,
--     add_edge() helper SP scheduled v0.5.0
--
-- Migration design (5 steps):
--   S1: pgmnemo.stats() SP with 13 health-signal columns
--   S2: recall_lessons() router rewritten — 3 new diagnostic columns appended
--       (vec_score, bm25_score, rrf_score)
--   S3: recency_weight default 0.08 → 0.05 (R1 code part)
--   S4: traverse_causal_chain 4-arg overload with deprecation NOTICE (R10)
--   S5: COMMENT refreshes citing the new defaults



-- ─────────────────────────────────────────────────────────────────────────────
-- S1: pgmnemo.stats() — diagnostic health-check SP
-- RFC R3 + maintainer additions (recall_hybrid_available,
-- oldest_lesson_age_days, orphan_count for R7).
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.stats()
RETURNS TABLE (
    version                    TEXT,
    lesson_count               BIGINT,
    embedded_count             BIGINT,
    embedding_coverage_pct     DOUBLE PRECISION,
    tsv_coverage_pct           DOUBLE PRECISION,
    mem_edge_count             BIGINT,
    recency_weight             DOUBLE PRECISION,
    ef_search                  INT,
    importance_weight          DOUBLE PRECISION,
    hybrid_enabled             BOOLEAN,
    recall_hybrid_available    BOOLEAN,
    oldest_lesson_age_days     INT,
    orphan_count               BIGINT
)
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $$
    SELECT
        pgmnemo.version() AS version,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.agent_lesson) AS lesson_count,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.agent_lesson WHERE embedding IS NOT NULL) AS embedded_count,
        (SELECT CASE WHEN COUNT(*) > 0
                     THEN ROUND(100.0 * SUM(CASE WHEN embedding IS NOT NULL THEN 1 ELSE 0 END)::NUMERIC / COUNT(*), 2)::DOUBLE PRECISION
                     ELSE 0.0::DOUBLE PRECISION END
         FROM pgmnemo.agent_lesson) AS embedding_coverage_pct,
        (SELECT CASE WHEN COUNT(*) > 0
                     THEN ROUND(100.0 * SUM(CASE WHEN lesson_tsv IS NOT NULL THEN 1 ELSE 0 END)::NUMERIC / COUNT(*), 2)::DOUBLE PRECISION
                     ELSE 0.0::DOUBLE PRECISION END
         FROM pgmnemo.agent_lesson) AS tsv_coverage_pct,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.mem_edge) AS mem_edge_count,
        COALESCE(NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05) AS recency_weight,
        COALESCE(NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100) AS ef_search,
        COALESCE(NULLIF(current_setting('pgmnemo.importance_weight', TRUE), '')::DOUBLE PRECISION, 0.15) AS importance_weight,
        NOT COALESCE(current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE) AS hybrid_enabled,
        EXISTS (
            SELECT 1 FROM pg_proc p
            JOIN pg_namespace n ON n.oid = p.pronamespace
            WHERE n.nspname = 'pgmnemo' AND p.proname = 'recall_hybrid'
        ) AS recall_hybrid_available,
        (SELECT COALESCE(
            EXTRACT(EPOCH FROM (NOW() - MIN(created_at))) / 86400.0,
            0
        )::INT FROM pgmnemo.agent_lesson) AS oldest_lesson_age_days,
        (
            SELECT COUNT(*)::BIGINT
            FROM pg_proc p
            JOIN pg_namespace n ON n.oid = p.pronamespace
            LEFT JOIN pg_depend d
                ON d.objid = p.oid
               AND d.deptype = 'e'
               AND d.refobjid = (SELECT oid FROM pg_extension WHERE extname = 'pgmnemo')
            WHERE n.nspname = 'pgmnemo'
              AND p.proname NOT LIKE '\_%' ESCAPE '\'   -- exclude private _foo() helpers
              AND d.objid IS NULL
        ) AS orphan_count;
$$;

COMMENT ON FUNCTION pgmnemo.stats() IS
    'v0.4.1 diagnostic health-check (RFC R3). Single-row summary of '
    'corpus size, embedding/tsvector coverage, GUC values, hybrid availability, '
    'and orphan-function count (functions in pgmnemo schema not owned by the '
    'extension — typically caused by intermediate manual SQL patches; see '
    'docs/MIGRATION.md §B.5 for recovery). Safe to call from monitoring loops; '
    '<50ms on N=10k corpus.';


-- ─────────────────────────────────────────────────────────────────────────────
-- S2: recall_lessons() router — adds 3 diagnostic columns (R4)
-- vec_score, bm25_score, rrf_score appended at end of return row.
-- Backward compatible for named-column callers; positional callers re-audit.
--
-- Return type changed (12 → 15 cols) → must DROP before CREATE OR REPLACE.
-- PostgreSQL refuses to alter the row-type of a function in-place.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT     DEFAULT 10,
    role_filter       TEXT    DEFAULT NULL,
    project_id_filter INT     DEFAULT NULL,
    query_text        TEXT    DEFAULT NULL
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ,
    -- v0.4.1 diagnostic columns (R4):
    vec_score     DOUBLE PRECISION,
    bm25_score    DOUBLE PRECISION,
    rrf_score     DOUBLE PRECISION
)
LANGUAGE plpgsql
VOLATILE
AS $$
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
BEGIN
    -- Routing decision (v0.4.0): query_text + embedding + not disabled → hybrid
    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _disable_hybrid := FALSE;
    END;

    IF NOT _disable_hybrid
       AND query_text IS NOT NULL
       AND length(trim(query_text)) > 0
       AND query_embedding IS NOT NULL THEN
        -- Hybrid path: project 15 cols from recall_hybrid's 15-col output
        RETURN QUERY
        SELECT
            h.lesson_id,
            h.score,
            h.role,
            h.project_id,
            h.topic,
            h.lesson_text,
            h.importance,
            h.metadata,
            h.commit_sha,
            h.artifact_hash,
            h.verified_at,
            h.created_at,
            h.vec_score,
            h.bm25_score,
            h.rrf_score
        FROM pgmnemo.recall_hybrid(
            query_embedding,
            query_text,
            k,
            role_filter,
            project_id_filter,
            0.4,    -- vec_weight
            0.4,    -- bm25_weight
            60      -- rrf_k
        ) h;
        RETURN;
    END IF;

    -- Vector-only path: unchanged formula, but populate diagnostic cols with
    -- vec_score = raw cosine (from formula), bm25_score = NULL, rrf_score = NULL.
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT,
            100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION,
        0.05  -- v0.4.1: default lowered from 0.08 per an internal ablation (R1)
    );

    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE candidates AS (
        SELECT
            al.id AS cand_id,
            al.role AS cand_role,
            al.project_id AS cand_project_id,
            al.topic AS cand_topic,
            al.lesson_text AS cand_lesson_text,
            al.importance AS cand_importance,
            al.metadata AS cand_metadata,
            al.commit_sha AS cand_commit_sha,
            al.artifact_hash AS cand_artifact_hash,
            al.verified_at AS cand_verified_at,
            al.created_at AS cand_created_at,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS vec_score_raw
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
        ORDER BY al.embedding <=> query_embedding
        LIMIT GREATEST(k * 5, 50)
    ),
    anchors AS (
        SELECT cand_id FROM candidates ORDER BY vec_score_raw DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT cand_id, 0, cand_id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.relation_type IN ('CAUSED_BY', 'CO_OCCURRED', 'DERIVED_FROM')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT reached_id AS gp_lesson_id,
               MAX(1.0 - depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk WHERE depth > 0 GROUP BY reached_id
    )
    SELECT
        c.cand_id AS lesson_id,
        (
            0.5 * c.vec_score_raw
          + 0.2 * (c.cand_importance::DOUBLE PRECISION / 5.0)
          + _gamma * GREATEST(0.0, 1.0 - LEAST(
                EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0
            ))
          + 0.1 * (CASE
                WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
                ELSE 0.0 END)
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        ) AS score,
        c.cand_role AS role,
        c.cand_project_id AS project_id,
        c.cand_topic AS topic,
        c.cand_lesson_text AS lesson_text,
        c.cand_importance AS importance,
        c.cand_metadata AS metadata,
        c.cand_commit_sha AS commit_sha,
        c.cand_artifact_hash AS artifact_hash,
        c.cand_verified_at AS verified_at,
        c.cand_created_at AS created_at,
        c.vec_score_raw AS vec_score,
        NULL::DOUBLE PRECISION AS bm25_score,
        NULL::DOUBLE PRECISION AS rrf_score
    FROM candidates c
    LEFT JOIN graph_proximity gp ON gp.gp_lesson_id = c.cand_id
    ORDER BY (
        0.5 * c.vec_score_raw
      + 0.2 * (c.cand_importance::DOUBLE PRECISION / 5.0)
      + _gamma * GREATEST(0.0, 1.0 - LEAST(
            EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0
        ))
      + 0.1 * (CASE
            WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
            WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
            ELSE 0.0 END)
      + _graph_weight * COALESCE(gp.proximity, 0.0)
    ) DESC,
    c.cand_importance DESC,
    c.cand_created_at DESC
    LIMIT k;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT) IS
    'v0.4.1 hybrid router with diagnostic columns. Routes to recall_hybrid() '
    'when query_text non-empty AND embedding present AND pgmnemo.disable_hybrid '
    'is FALSE/unset. Vector-only path uses §6.4 scoring with γ = '
    'pgmnemo.recency_weight (default 0.05 since v0.4.1 per an internal ablation). '
    'Diagnostic columns (v0.4.1, R4): vec_score = raw cosine; bm25_score / '
    'rrf_score = NULL on vector-only path, populated on hybrid path. '
    'Opt-out: SET pgmnemo.disable_hybrid = ''true''.';


-- ─────────────────────────────────────────────────────────────────────────────
-- S4: traverse_causal_chain — 4-arg deprecation with NOTICE (RFC R10)
--
-- v0.4.0 ships only the 5-arg form with direction DEFAULT 'forward'. To support
-- existing 4-arg callers AND add a deprecation
-- NOTICE, we restructure both overloads:
--   - 5-arg: remove DEFAULT on direction (becomes explicit required parameter)
--   - 4-arg: new wrapper emitting RAISE NOTICE, delegating to 5-arg
--
-- This removes overload ambiguity (4-arg call → unambiguous match to 4-arg
-- wrapper) while preserving both call patterns.
--
-- Will be REMOVED in v0.5.0.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN, TEXT);
DROP FUNCTION IF EXISTS pgmnemo.traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN);

CREATE OR REPLACE FUNCTION pgmnemo.traverse_causal_chain(
    start_id        BIGINT,
    max_depth       INT,
    relation_types  TEXT[],
    only_active     BOOLEAN,
    direction       TEXT      -- v0.4.1: DEFAULT removed; required explicit value
)
RETURNS TABLE (
    lesson_id       BIGINT,
    depth           INT,
    path            BIGINT[],
    path_weight     REAL,
    role            TEXT,
    topic           TEXT,
    lesson_text     TEXT,
    importance      SMALLINT,
    created_at      TIMESTAMPTZ,
    commit_sha      TEXT,
    verified_at     TIMESTAMPTZ
)
LANGUAGE plpgsql
STABLE
PARALLEL SAFE
AS $$
BEGIN
    IF direction NOT IN ('forward', 'backward', 'both') THEN
        RAISE EXCEPTION
            'pgmnemo.traverse_causal_chain: direction must be ''forward'', ''backward'', or ''both'' — got: %',
            direction;
    END IF;

    RETURN QUERY
    WITH RECURSIVE causal_walk(lesson_id, depth, path, path_weight) AS (
        SELECT start_id, 0, ARRAY[start_id]::BIGINT[], 1.0::REAL
        UNION ALL
        SELECT
            CASE
                WHEN direction IN ('forward', 'both') AND me.source_id = cw.lesson_id THEN me.target_id
                WHEN direction IN ('backward', 'both') AND me.target_id = cw.lesson_id THEN me.source_id
            END,
            cw.depth + 1,
            cw.path ||
                CASE
                    WHEN direction IN ('forward', 'both') AND me.source_id = cw.lesson_id THEN me.target_id
                    WHEN direction IN ('backward', 'both') AND me.target_id = cw.lesson_id THEN me.source_id
                END,
            cw.path_weight * me.weight
        FROM causal_walk cw
        JOIN pgmnemo.mem_edge me ON (
            (direction IN ('forward', 'both') AND me.source_id = cw.lesson_id) OR
            (direction IN ('backward', 'both') AND me.target_id = cw.lesson_id)
        )
        WHERE cw.depth < max_depth
          AND me.relation_type = ANY(relation_types)
          AND NOT (
              CASE
                  WHEN direction IN ('forward', 'both') AND me.source_id = cw.lesson_id THEN me.target_id
                  WHEN direction IN ('backward', 'both') AND me.target_id = cw.lesson_id THEN me.source_id
              END = ANY(cw.path)
          )
    )
    SELECT
        cw.lesson_id,
        cw.depth,
        cw.path,
        cw.path_weight,
        al.role,
        al.topic,
        al.lesson_text,
        al.importance,
        al.created_at,
        al.commit_sha,
        al.verified_at
    FROM causal_walk cw
    JOIN pgmnemo.agent_lesson al ON al.id = cw.lesson_id
    WHERE cw.depth > 0
      AND (NOT only_active OR al.is_active)
    ORDER BY cw.depth, cw.path_weight DESC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN, TEXT) IS
    'v0.4.1 (canonical, since R10 deprecation cycle): BFS over mem_edge with '
    'explicit required direction (forward/backward/both). For callers from '
    'v0.4.0 or earlier that omitted direction (got DEFAULT ''forward'' silently), '
    'use the 4-arg deprecated wrapper or update calls to pass direction explicitly.';


-- 4-arg deprecation wrapper — emits NOTICE on every call.
CREATE OR REPLACE FUNCTION pgmnemo.traverse_causal_chain(
    start_id        BIGINT,
    max_depth       INT,
    relation_types  TEXT[],
    only_active     BOOLEAN
)
RETURNS TABLE (
    lesson_id       BIGINT,
    depth           INT,
    path            BIGINT[],
    path_weight     REAL,
    role            TEXT,
    topic           TEXT,
    lesson_text     TEXT,
    importance      SMALLINT,
    created_at      TIMESTAMPTZ,
    commit_sha      TEXT,
    verified_at     TIMESTAMPTZ
)
LANGUAGE plpgsql
STABLE
PARALLEL SAFE
AS $$
BEGIN
    RAISE NOTICE 'pgmnemo.traverse_causal_chain 4-arg overload is DEPRECATED in v0.4.1 and will be REMOVED in v0.5.0. Use the 5-arg form: traverse_causal_chain(start_id, max_depth, relation_types, only_active, direction). See CHANGELOG [0.4.1] for migration guidance.';
    RETURN QUERY
    SELECT * FROM pgmnemo.traverse_causal_chain(start_id, max_depth, relation_types, only_active, 'forward'::TEXT);
END;
$$;

COMMENT ON FUNCTION pgmnemo.traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN) IS
    'DEPRECATED in v0.4.1 (RFC R10). Wrapper around 5-arg form with '
    'direction=''forward''. Emits RAISE NOTICE on every call. Will be REMOVED '
    'in v0.5.0; update callers to pass direction explicitly.';


-- ─────────────────────────────────────────────────────────────────────────────
-- pgmnemo 0.5.0 additions (applied over 0.4.1 base above)
-- ─────────────────────────────────────────────────────────────────────────────

-- pgmnemo--0.4.1--0.5.0.sql
-- Migration: v0.4.1 → v0.5.0
--
-- Scope:
--   §A  R10 — Remove 4-arg traverse_causal_chain() overload (deprecated v0.4.1)
--   §B  H-06 — pgmnemo.temporal_boost GUC + get_temporal_boost() helper
--   §C  R5  — pgmnemo.max_query_text_chars GUC
--   §D  recall_lessons() — H-06 temporal_boost + R5 query_text truncation
--   §E  H-07 — Bitemporality (t_valid_from/t_valid_to/content_hash + trigger + view + as_of)
--   §F  ingest() — R5 lesson_text truncation guard
--   §G  R6  — pgmnemo.add_edge() idempotent edge upsert helper
--
-- All DDL is idempotent (IF NOT EXISTS / CREATE OR REPLACE / DROP IF EXISTS).
-- ─────────────────────────────────────────────────────────────────────────────

-- ─────────────────────────────────────────────────────────────────────────────
-- §A  R10: Remove 4-arg traverse_causal_chain() overload (deprecated v0.4.1)
--
-- The 4-arg form traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN) was
-- superseded by the 5-arg form (direction parameter added in v0.2.1).
-- Formally deprecated in v0.4.1; removed here in v0.5.0.
-- The 5-arg form traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN, TEXT)
-- is unchanged and remains the canonical interface.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.traverse_causal_chain(BIGINT, INT, TEXT[], BOOLEAN);

-- ─────────────────────────────────────────────────────────────────────────────
-- §B  H-06: pgmnemo.temporal_boost GUC + get_temporal_boost() helper
--
-- pgmnemo.temporal_boost (FLOAT, default 1.0, range 0.0–5.0):
--   Score multiplier for the recency component of recall_lessons().
--   effective_γ = pgmnemo.recency_weight × pgmnemo.temporal_boost
--   Default: 0.05 × 1.0 = 0.05 — backward compatible with v0.4.1.
--   H-06 optimal (cell C6): SET pgmnemo.temporal_boost = '10.0'
--     to reach effective_γ ≈ 0.5 with default recency_weight=0.05.
-- ─────────────────────────────────────────────────────────────────────────────

DO $$
BEGIN
    PERFORM set_config('pgmnemo.temporal_boost', '1.0', FALSE);
EXCEPTION WHEN OTHERS THEN
    NULL;
END;
$$;

CREATE OR REPLACE FUNCTION pgmnemo.get_temporal_boost()
RETURNS DOUBLE PRECISION
LANGUAGE plpgsql STABLE
AS $$
DECLARE
    _v DOUBLE PRECISION;
BEGIN
    _v := COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION,
        1.0
    );
    RETURN GREATEST(0.0, LEAST(20.0, _v));
END;
$$;

COMMENT ON FUNCTION pgmnemo.get_temporal_boost() IS
    'Returns pgmnemo.temporal_boost GUC (default 1.0, range 0.0–20.0). '
    'Score multiplier: effective_γ = recency_weight × temporal_boost. '
    'H-06 optimal (C6): boost=10 with rw=0.05 → effective_γ=0.5; max boost=20 for aggressive recency. '
    'H-06 (v0.5.0).';

-- ─────────────────────────────────────────────────────────────────────────────
-- §C  R5: pgmnemo.max_query_text_chars GUC
--
-- Limits query_text in recall_lessons() and lesson_text in ingest().
-- Default 2000 chars. Long text is truncated with RAISE NOTICE.
-- Set to 0 or negative to disable truncation entirely.
-- ─────────────────────────────────────────────────────────────────────────────

DO $$
BEGIN
    PERFORM set_config('pgmnemo.max_query_text_chars', '2000', FALSE);
EXCEPTION WHEN OTHERS THEN
    NULL;
END;
$$;

-- ─────────────────────────────────────────────────────────────────────────────
-- §D  recall_lessons() — H-06 temporal_boost + R5 query_text cap
--
-- Changes from v0.4.1:
--   H-06: effective_γ = pgmnemo.recency_weight × pgmnemo.temporal_boost
--         (backward-compatible default 0.05 × 1.0 = 0.05).
--   R5:   query_text truncated to pgmnemo.max_query_text_chars (default 2000)
--         before ts_query/embedding use; RAISE NOTICE on truncation.
-- ─────────────────────────────────────────────────────────────────────────────


DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT           DEFAULT 10,
    role_filter       TEXT          DEFAULT NULL,
    project_id_filter INT           DEFAULT NULL,
    query_text        TEXT          DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ   DEFAULT NULL  -- v0.6.1 F2: point-in-time recall
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ,
    vec_score     DOUBLE PRECISION,
    bm25_score    DOUBLE PRECISION,
    rrf_score     DOUBLE PRECISION
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
BEGIN
    -- R5: clamp query_text to pgmnemo.max_query_text_chars (default 2000).
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT,
        2000
    );
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars '
                     '(pgmnemo.max_query_text_chars). Original length: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _disable_hybrid := FALSE;
    END;

    IF NOT _disable_hybrid
       AND _query_text IS NOT NULL
       AND length(trim(_query_text)) > 0
       AND query_embedding IS NOT NULL THEN

        -- v0.6.1 F2: propagate as_of_ts to recall_hybrid() via GUC (transaction-local)
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        RETURN QUERY
        SELECT
            h.lesson_id,
            h.score,
            h.role,
            h.project_id,
            h.topic,
            h.lesson_text,
            h.importance,
            h.metadata,
            h.commit_sha,
            h.artifact_hash,
            h.verified_at,
            h.created_at,
            h.vec_score,
            h.bm25_score,
            h.rrf_score
        FROM pgmnemo.recall_hybrid(
            query_embedding,
            _query_text,
            k,
            role_filter,
            project_id_filter,
            0.4,
            0.4,
            60
        ) h;
        RETURN;
    END IF;

    -- Vector-only path
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT,
            100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    -- Base recency weight γ (backward compat default 0.05).
    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION,
        0.05
    );

    -- H-06: recency decay coefficient = recency_weight × temporal_boost
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION,
        1.0
    )));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', _query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', _query_text);
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE candidates AS (
        SELECT
            al.id AS cand_id,
            al.role AS cand_role,
            al.project_id AS cand_project_id,
            al.topic AS cand_topic,
            al.lesson_text AS cand_lesson_text,
            al.importance AS cand_importance,
            al.metadata AS cand_metadata,
            al.commit_sha AS cand_commit_sha,
            al.artifact_hash AS cand_artifact_hash,
            al.verified_at AS cand_verified_at,
            al.created_at AS cand_created_at,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS vec_score_raw
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
          -- v0.6.1 F2: point-in-time filter on vector-only path
          AND (as_of_ts IS NULL OR (al.t_valid_from <= as_of_ts AND al.t_valid_to > as_of_ts))
        ORDER BY al.embedding <=> query_embedding
        LIMIT GREATEST(k * 5, 50)
    ),
    anchors AS (
        SELECT cand_id FROM candidates ORDER BY vec_score_raw DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT cand_id, 0, cand_id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.relation_type IN ('CAUSED_BY', 'CO_OCCURRED', 'DERIVED_FROM')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT reached_id AS gp_lesson_id,
               MAX(1.0 - depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk WHERE depth > 0 GROUP BY reached_id
    )
    SELECT
        c.cand_id AS lesson_id,
        (
            0.5 * c.vec_score_raw
          + 0.2 * (c.cand_importance::DOUBLE PRECISION / 5.0)
          + _gamma * GREATEST(0.0, 1.0 - LEAST(
                EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0
            ))
          + 0.1 * (CASE
                WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
                ELSE 0.0 END)
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        ) AS score,
        c.cand_role AS role,
        c.cand_project_id AS project_id,
        c.cand_topic AS topic,
        c.cand_lesson_text AS lesson_text,
        c.cand_importance AS importance,
        c.cand_metadata AS metadata,
        c.cand_commit_sha AS commit_sha,
        c.cand_artifact_hash AS artifact_hash,
        c.cand_verified_at AS verified_at,
        c.cand_created_at AS created_at,
        c.vec_score_raw AS vec_score,
        NULL::DOUBLE PRECISION AS bm25_score,
        NULL::DOUBLE PRECISION AS rrf_score
    FROM candidates c
    LEFT JOIN graph_proximity gp ON gp.gp_lesson_id = c.cand_id
    ORDER BY (
        0.5 * c.vec_score_raw
      + 0.2 * (c.cand_importance::DOUBLE PRECISION / 5.0)
      + _gamma * GREATEST(0.0, 1.0 - LEAST(
            EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0
        ))
      + 0.1 * (CASE
            WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
            WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
            ELSE 0.0 END)
      + _graph_weight * COALESCE(gp.proximity, 0.0)
    ) DESC,
    c.cand_importance DESC,
    c.cand_created_at DESC
    LIMIT k;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ) IS
    'v0.6.3 — R1 AmbiguousColumn fix (#variable_conflict use_column). '
    'v0.6.2 hybrid router with as_of_ts point-in-time parameter (F2). '
    'as_of_ts DEFAULT NULL preserves v0.5.1/v0.6.0 behavior at existing call sites. '
    'When as_of_ts IS NOT NULL: propagates to recall_hybrid() via pgmnemo.as_of_timestamp GUC '
    '  (transaction-local SET, TRUE flag = local to transaction); '
    '  vector-only path applies filter directly in candidates CTE WHERE clause. '
    'R5: query_text truncated to pgmnemo.max_query_text_chars (default 2000) with RAISE NOTICE. '
    'H-06: recency decay = max(0, 1 - age_days/90); coeff=recency_weight×temporal_boost. '
    'Diagnostic cols: vec_score=cosine; bm25_score/rrf_score=NULL on vector-only path.';


-- ─────────────────────────────────────────────────────────────────────────────
-- Update recall_lessons_pooled() to call 6-arg recall_lessons()
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons_pooled(
    query_embedding  vector(1024),
    k                INT  DEFAULT 10,
    app_id           INT  DEFAULT NULL
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $$
    SELECT lesson_id, score, role, project_id, topic, lesson_text,
           importance, metadata, commit_sha, artifact_hash, verified_at, created_at
    FROM pgmnemo.recall_lessons(query_embedding, k, NULL::TEXT, app_id, NULL::TEXT, NULL::TIMESTAMPTZ);
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons_pooled(vector, INT, INT) IS
    'Cross-role recall wrapper: calls recall_lessons() with role=NULL (pooled — no role filter). '
    'v0.6.2: calls 6-arg recall_lessons() with NULL as_of_ts.';

-- ─────────────────────────────────────────────────────────────────────────────
-- §E  H-07: Bitemporality primitive on agent_lesson (v0.5.0)
--
-- Adds valid-time columns (t_valid_from, t_valid_to) + computed dedup key
-- (content_hash) to pgmnemo.agent_lesson.  All DDL is idempotent.
-- Active rows: t_valid_to = 'infinity'.
-- On INSERT of a row with the same content_hash, trigger closes the prior.
-- pgmnemo.mem_item: active-only view alias.
-- pgmnemo.as_of(ts): point-in-time query.
-- ─────────────────────────────────────────────────────────────────────────────

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS t_valid_from  TIMESTAMPTZ NOT NULL DEFAULT now(),
    ADD COLUMN IF NOT EXISTS t_valid_to    TIMESTAMPTZ NOT NULL DEFAULT 'infinity'::TIMESTAMPTZ;

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS content_hash  TEXT GENERATED ALWAYS AS (
        MD5(
            COALESCE(role,        '') || '|' ||
            COALESCE(topic,       '') || '|' ||
            COALESCE(commit_sha, COALESCE(artifact_hash, ''))
        )
    ) STORED;

COMMENT ON COLUMN pgmnemo.agent_lesson.t_valid_from IS
    'Valid-time start. Defaults to row creation; backfilled to created_at for pre-H-07 rows. H-07 (v0.5.0).';
COMMENT ON COLUMN pgmnemo.agent_lesson.t_valid_to IS
    '''infinity'' = currently active row. Set to now() by trigger on conflicting insert. H-07 (v0.5.0).';
COMMENT ON COLUMN pgmnemo.agent_lesson.content_hash IS
    'MD5(role|topic|commit_sha_or_artifact_hash). Computed dedup key for bitemporal trigger. H-07 (v0.5.0).';

-- Backfill: existing rows get t_valid_from = created_at.
-- WHERE guard: only rows whose t_valid_from was just set to now() by the ADD COLUMN DEFAULT.
UPDATE pgmnemo.agent_lesson
SET    t_valid_from = created_at
WHERE  t_valid_from >= (now() - INTERVAL '1 second');

CREATE INDEX IF NOT EXISTS ix_agent_lesson_valid_range
    ON pgmnemo.agent_lesson (t_valid_from, t_valid_to)
    WHERE t_valid_to = 'infinity'::TIMESTAMPTZ;

CREATE INDEX IF NOT EXISTS ix_agent_lesson_content_hash_active
    ON pgmnemo.agent_lesson (content_hash)
    WHERE t_valid_to = 'infinity'::TIMESTAMPTZ;

CREATE OR REPLACE FUNCTION pgmnemo._bitemporal_close_prior()
RETURNS TRIGGER
LANGUAGE plpgsql
AS $$
BEGIN
    IF NEW.content_hash IS NOT NULL THEN
        UPDATE pgmnemo.agent_lesson
        SET    t_valid_to = now()
        WHERE  content_hash = NEW.content_hash
          AND  t_valid_to   = 'infinity'::TIMESTAMPTZ
          AND  id           <> NEW.id;
    END IF;
    RETURN NEW;
END;
$$;

DROP TRIGGER IF EXISTS trg_agent_lesson_bitemporal_close ON pgmnemo.agent_lesson;
CREATE TRIGGER trg_agent_lesson_bitemporal_close
    AFTER INSERT ON pgmnemo.agent_lesson
    FOR EACH ROW
    EXECUTE FUNCTION pgmnemo._bitemporal_close_prior();

CREATE OR REPLACE VIEW pgmnemo.mem_item AS
    SELECT * FROM pgmnemo.agent_lesson
    WHERE  t_valid_to = 'infinity'::TIMESTAMPTZ;

COMMENT ON VIEW pgmnemo.mem_item IS
    'Active-only alias for pgmnemo.agent_lesson (t_valid_to = infinity). H-07 (v0.5.0).';

CREATE OR REPLACE FUNCTION pgmnemo.as_of(ts TIMESTAMPTZ)
RETURNS SETOF pgmnemo.agent_lesson
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $$
    SELECT *
    FROM   pgmnemo.agent_lesson
    WHERE  t_valid_from <= ts
      AND  t_valid_to   >  ts;
$$;

COMMENT ON FUNCTION pgmnemo.as_of(TIMESTAMPTZ) IS
    'Time-travel: rows active at ts (t_valid_from <= ts < t_valid_to). H-07 (v0.5.0).';

-- ─────────────────────────────────────────────────────────────────────────────
-- §F  R5: ingest() — lesson_text truncation guard
--
-- Replaces v0.4.1 ingest() with R5 truncation on p_lesson_text.
-- NULL/empty lesson_text: RAISE NOTICE then proceed to DB constraint.
-- Truncation threshold: pgmnemo.max_query_text_chars (default 2000).
-- Set GUC to 0 or negative to disable.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.ingest(
    p_role          TEXT,
    p_project_id    INT,
    p_topic         TEXT,
    p_lesson_text   TEXT,
    p_importance    SMALLINT     DEFAULT 3,
    p_embedding     vector(1024) DEFAULT NULL,
    p_commit_sha    TEXT         DEFAULT NULL,
    p_artifact_hash TEXT         DEFAULT NULL,
    p_metadata      JSONB        DEFAULT '{}'::jsonb
) RETURNS BIGINT
LANGUAGE plpgsql AS $$
DECLARE
    new_id      BIGINT;
    _max_chars  INT;
BEGIN
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT,
        2000
    );

    IF p_lesson_text IS NULL OR length(trim(p_lesson_text)) = 0 THEN
        RAISE NOTICE 'pgmnemo.ingest: p_lesson_text is NULL or empty — proceeding.';
    ELSIF _max_chars > 0 AND length(p_lesson_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.ingest: p_lesson_text truncated to % chars '
                     '(pgmnemo.max_query_text_chars). Original length: %',
                     _max_chars, length(p_lesson_text);
        p_lesson_text := left(p_lesson_text, _max_chars);
    END IF;

    IF p_embedding IS NOT NULL AND vector_dims(p_embedding) <> 1024 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: embedding dimension mismatch — expected 1024, got %',
            vector_dims(p_embedding);
    END IF;

    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance, embedding,
        commit_sha, artifact_hash, metadata, verified_at
    ) VALUES (
        p_role, p_project_id, p_topic, p_lesson_text, p_importance, p_embedding,
        p_commit_sha, p_artifact_hash, p_metadata,
        CASE WHEN p_commit_sha IS NOT NULL OR p_artifact_hash IS NOT NULL
             THEN NOW() ELSE NULL END
    ) RETURNING id INTO new_id;

    RETURN new_id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.ingest(TEXT, INT, TEXT, TEXT, SMALLINT, vector, TEXT, TEXT, JSONB) IS
    'Validated public write API (v0.5.0 + R5). '
    'Truncates p_lesson_text to pgmnemo.max_query_text_chars (default 2000) with RAISE NOTICE. '
    'NULL/empty p_lesson_text: emits NOTICE, proceeds to DB constraint. '
    'Set GUC to 0 or negative to disable truncation. R5 (v0.5.0).';

-- ─────────────────────────────────────────────────────────────────────────────
-- §G  R6: pgmnemo.add_edge() — idempotent edge upsert helper (v0.5.0)
--
-- Convenience wrapper for INSERT ... ON CONFLICT on pgmnemo.mem_edge.
-- On conflict on (source_id, target_id, relation_type WHERE valid_until IS NULL),
-- updates weight and metadata per p_mode.
--
-- Parameters:
--   p_source_id     BIGINT  — FK → pgmnemo.agent_lesson.id (NOT NULL enforced by table)
--   p_target_id     BIGINT  — FK → pgmnemo.agent_lesson.id (NOT NULL enforced by table)
--   p_relation_type TEXT    — e.g. CAUSED_BY, CO_OCCURRED, DERIVED_FROM, ENTITY_LINK
--   p_weight        FLOAT8  — clamped to [0.0, 1.0], default 1.0
--   p_metadata      JSONB   — default '{}'
--   p_mode          TEXT    — 'replace' (default) | 'max' | 'avg'
--
-- edge_kind auto-derived from p_relation_type (SQL_REFERENCE §1.1 canonical mapping).
-- NULL p_source_id / p_target_id → NOT NULL constraint violation from mem_edge.
-- Unknown id → FK constraint violation from mem_edge.
-- ─────────────────────────────────────────────────────────────────────────────

-- Partial unique index enabling ON CONFLICT for active-edge upsert.
CREATE UNIQUE INDEX IF NOT EXISTS uq_mem_edge_active
    ON pgmnemo.mem_edge (source_id, target_id, relation_type)
    WHERE valid_until IS NULL;

CREATE OR REPLACE FUNCTION pgmnemo.add_edge(
    p_source_id     BIGINT,
    p_target_id     BIGINT,
    p_relation_type TEXT,
    p_weight        FLOAT8  DEFAULT 1.0,
    p_metadata      JSONB   DEFAULT '{}'::jsonb,
    p_mode          TEXT    DEFAULT 'replace'
) RETURNS VOID
LANGUAGE plpgsql
AS $$
DECLARE
    _edge_kind pgmnemo.edge_kind;
    _weight    REAL := GREATEST(0.0, LEAST(1.0, COALESCE(p_weight, 1.0)));
BEGIN
    _edge_kind := CASE
        WHEN p_relation_type IN ('CAUSED_BY', 'DERIVED_FROM', 'CONTRADICTS')
            THEN 'causal'::pgmnemo.edge_kind
        WHEN p_relation_type IN ('CO_OCCURRED', 'PRECEDED_BY')
            THEN 'temporal'::pgmnemo.edge_kind
        WHEN p_relation_type IN ('ENTITY_LINK', 'SHARED_TAG', 'IS_A', 'PART_OF')
            THEN 'entity'::pgmnemo.edge_kind
        ELSE 'semantic'::pgmnemo.edge_kind
    END;

    IF p_mode NOT IN ('replace', 'max', 'avg') THEN
        RAISE EXCEPTION
            'pgmnemo.add_edge: unknown mode ''%'' — valid values: replace, max, avg',
            p_mode;
    END IF;

    IF p_mode = 'max' THEN
        INSERT INTO pgmnemo.mem_edge
            (source_id, target_id, relation_type, edge_kind, weight, metadata)
        VALUES
            (p_source_id, p_target_id, p_relation_type, _edge_kind,
             _weight, COALESCE(p_metadata, '{}'))
        ON CONFLICT (source_id, target_id, relation_type) WHERE valid_until IS NULL
        DO UPDATE SET
            weight     = GREATEST(pgmnemo.mem_edge.weight, EXCLUDED.weight),
            metadata   = pgmnemo.mem_edge.metadata || EXCLUDED.metadata,
            updated_at = now();

    ELSIF p_mode = 'avg' THEN
        INSERT INTO pgmnemo.mem_edge
            (source_id, target_id, relation_type, edge_kind, weight, metadata)
        VALUES
            (p_source_id, p_target_id, p_relation_type, _edge_kind,
             _weight, COALESCE(p_metadata, '{}'))
        ON CONFLICT (source_id, target_id, relation_type) WHERE valid_until IS NULL
        DO UPDATE SET
            weight     = (pgmnemo.mem_edge.weight + EXCLUDED.weight) / 2.0,
            metadata   = pgmnemo.mem_edge.metadata || EXCLUDED.metadata,
            updated_at = now();

    ELSE
        INSERT INTO pgmnemo.mem_edge
            (source_id, target_id, relation_type, edge_kind, weight, metadata)
        VALUES
            (p_source_id, p_target_id, p_relation_type, _edge_kind,
             _weight, COALESCE(p_metadata, '{}'))
        ON CONFLICT (source_id, target_id, relation_type) WHERE valid_until IS NULL
        DO UPDATE SET
            weight     = EXCLUDED.weight,
            metadata   = EXCLUDED.metadata,
            updated_at = now();
    END IF;
END;
$$;

COMMENT ON FUNCTION pgmnemo.add_edge(BIGINT, BIGINT, TEXT, FLOAT8, JSONB, TEXT) IS
    'Idempotent edge upsert helper (R6, v0.5.0). '
    'Inserts or updates a directed typed edge in pgmnemo.mem_edge. '
    'edge_kind auto-derived from p_relation_type (SQL_REFERENCE §1.1). '
    'Conflict on uq_mem_edge_active (source_id, target_id, relation_type WHERE valid_until IS NULL). '
    'p_mode: ''replace'' (last-writer-wins) | ''max'' (monotonic weight) | ''avg'' (running mean). '
    'p_weight clamped to [0.0, 1.0]. '
    'NULL source_id/target_id → NOT NULL violation; unknown id → FK violation. R6 (v0.5.0).';
-- pgmnemo 0.5.1 → 0.6.0 upgrade migration
-- Changes:
--   §3  pgmnemo.stats()   — add ghost_count BIGINT column
--   §4  pgmnemo.ingest()  — RAISE NOTICE when bitemporal close+create fires (Q5 dedup obs.)
-- Backward compatibility: zero breaking changes (see PLAN_V060.md §2 audit).
-- Prerequisites: pgmnemo 0.5.1 installed. Requires pgvector.
-- Apply: ALTER EXTENSION pgmnemo UPDATE TO '0.6.0';

-- ─────────────────────────────────────────────────────────────────────────────
-- §1, §2 (recall_hybrid + recall_lessons changes) deferred to v0.6.1.
--
-- The original v0.6.0 plan added bitemporal `as_of_ts` parameter to
-- recall_lessons() and made recall_hybrid() read pgmnemo.as_of_timestamp
-- GUC for temporal filtering. Implementation introduced a CTE refactor
-- that caused two runtime regressions (AmbiguousColumn on `role`,
-- UndefinedTable on `graph_walk`) caught by scripts/smoke_recall_hybrid.py.
--
-- Decision: ship v0.6.0 with the additive, side-effect-free items only
-- (ghost_count, NOTICE, recall_stats view, docs). recall_hybrid() and
-- recall_lessons() remain byte-identical to v0.5.1 — Δ=0 confirmed.
--
-- Bitemporal recall returns in v0.6.1 alongside the corrected RRF
-- variant (A-scale) after real-DB benchmark validation.
-- See spec/v060/INVESTIGATION_FIX_A_REGRESSION.md for context.

-- §3  pgmnemo.stats() — add ghost_count BIGINT (RFC Q4)
--
-- Return type changes (13 → 14 cols) → must DROP before CREATE.
-- ghost_count: active lessons with verified_at IS NULL (no provenance).
-- Distinct from orphan_count (functions not owned by extension).
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.stats();

CREATE OR REPLACE FUNCTION pgmnemo.stats()
RETURNS TABLE (
    version                    TEXT,
    lesson_count               BIGINT,
    embedded_count             BIGINT,
    embedding_coverage_pct     DOUBLE PRECISION,
    tsv_coverage_pct           DOUBLE PRECISION,
    mem_edge_count             BIGINT,
    recency_weight             DOUBLE PRECISION,
    ef_search                  INT,
    importance_weight          DOUBLE PRECISION,
    hybrid_enabled             BOOLEAN,
    recall_hybrid_available    BOOLEAN,
    oldest_lesson_age_days     INT,
    orphan_count               BIGINT,
    ghost_count                BIGINT     -- NEW v0.6.0: active lessons without provenance
)
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $$
    SELECT
        pgmnemo.version()                                                          AS version,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.agent_lesson)                        AS lesson_count,
        (SELECT COUNT(*)::BIGINT
         FROM pgmnemo.agent_lesson WHERE embedding IS NOT NULL)                    AS embedded_count,
        (SELECT CASE WHEN COUNT(*) > 0
                     THEN ROUND(100.0 *
                                SUM(CASE WHEN embedding IS NOT NULL THEN 1 ELSE 0 END)::NUMERIC
                                / COUNT(*), 2)::DOUBLE PRECISION
                     ELSE 0.0 END
         FROM pgmnemo.agent_lesson)                                                AS embedding_coverage_pct,
        (SELECT CASE WHEN COUNT(*) > 0
                     THEN ROUND(100.0 *
                                SUM(CASE WHEN lesson_tsv IS NOT NULL THEN 1 ELSE 0 END)::NUMERIC
                                / COUNT(*), 2)::DOUBLE PRECISION
                     ELSE 0.0 END
         FROM pgmnemo.agent_lesson)                                                AS tsv_coverage_pct,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.mem_edge)                            AS mem_edge_count,
        COALESCE(NULLIF(current_setting('pgmnemo.recency_weight',  TRUE), '')::DOUBLE PRECISION,
                 0.05)                                                             AS recency_weight,
        COALESCE(NULLIF(current_setting('pgmnemo.ef_search',       TRUE), '')::INT,
                 100)                                                              AS ef_search,
        COALESCE(NULLIF(current_setting('pgmnemo.importance_weight',TRUE), '')::DOUBLE PRECISION,
                 0.15)                                                             AS importance_weight,
        NOT COALESCE(current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN,
                     FALSE)                                                        AS hybrid_enabled,
        EXISTS (
            SELECT 1 FROM pg_proc p
            JOIN pg_namespace n ON n.oid = p.pronamespace
            WHERE n.nspname = 'pgmnemo' AND p.proname = 'recall_hybrid'
        )                                                                          AS recall_hybrid_available,
        (SELECT COALESCE(
                    EXTRACT(EPOCH FROM (NOW() - MIN(created_at))) / 86400.0, 0
                )::INT
         FROM pgmnemo.agent_lesson)                                                AS oldest_lesson_age_days,
        -- orphan_count: pgmnemo-schema functions not owned by the extension
        (SELECT COUNT(*)::BIGINT
         FROM pg_proc p
         JOIN pg_namespace n ON n.oid = p.pronamespace
         LEFT JOIN pg_depend d
             ON d.objid = p.oid AND d.deptype = 'e'
            AND d.refobjid = (SELECT oid FROM pg_extension WHERE extname = 'pgmnemo')
         WHERE n.nspname = 'pgmnemo'
           AND p.proname NOT LIKE '\_%' ESCAPE '\'
           AND d.objid IS NULL)                                                    AS orphan_count,
        -- ghost_count (v0.6.0): active lessons without provenance (RFC Q4)
        -- Definition: t_valid_to = 'infinity' (authoritative active-row indicator) AND verified_at IS NULL.
        -- NOTE: the RFC Q4 spec says "is_active = TRUE"; implementation uses t_valid_to = 'infinity'
        -- because _bitemporal_close_prior() trigger does NOT update is_active when closing rows.
        -- t_valid_to = 'infinity' is the correct semantic equivalent of "currently active".
        (SELECT COUNT(*)::BIGINT
         FROM pgmnemo.agent_lesson
         WHERE verified_at IS NULL
           AND t_valid_to = 'infinity'::TIMESTAMPTZ)                               AS ghost_count;
$$;

COMMENT ON FUNCTION pgmnemo.stats() IS
    'v0.6.0 diagnostic health-check. Adds ghost_count (RFC Q4). '
    'ghost_count: active lessons where verified_at IS NULL — no commit_sha AND no artifact_hash. '
    'Distinct from orphan_count (functions not owned by extension). '
    'Use ghost_count to track provenance migration progress: target < 5% of lesson_count '
    'before switching pgmnemo.include_unverified = off. '
    'Single-row summary; <50ms on N=10k corpus.';


-- ─────────────────────────────────────────────────────────────────────────────
-- §4  pgmnemo.ingest() — RAISE NOTICE on bitemporal close+create (RFC Q5)
--
-- Pre-insert content_hash check; RAISE NOTICE if dedup close fires.
-- content_hash formula matches GENERATED ALWAYS AS column:
--   MD5(COALESCE(role,'') || '|' || COALESCE(topic,'') || '|' || COALESCE(commit_sha, artifact_hash, ''))
-- No signature change → CREATE OR REPLACE sufficient.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.ingest(
    p_role          TEXT,
    p_project_id    INT,
    p_topic         TEXT,
    p_lesson_text   TEXT,
    p_importance    SMALLINT     DEFAULT 3,
    p_embedding     vector(1024) DEFAULT NULL,
    p_commit_sha    TEXT         DEFAULT NULL,
    p_artifact_hash TEXT         DEFAULT NULL,
    p_metadata      JSONB        DEFAULT '{}'::jsonb
) RETURNS BIGINT
LANGUAGE plpgsql AS $$
DECLARE
    new_id          BIGINT;
    _max_chars      INT;
    _content_hash   TEXT;
    _prior_count    INT;
BEGIN
    -- R5: clamp lesson_text
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT,
        2000
    );
    IF p_lesson_text IS NULL OR length(trim(p_lesson_text)) = 0 THEN
        RAISE NOTICE 'pgmnemo.ingest: p_lesson_text is NULL or empty — proceeding.';
    ELSIF _max_chars > 0 AND length(p_lesson_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.ingest: p_lesson_text truncated to % chars '
                     '(pgmnemo.max_query_text_chars). Original length: %',
                     _max_chars, length(p_lesson_text);
        p_lesson_text := left(p_lesson_text, _max_chars);
    END IF;

    IF p_embedding IS NOT NULL AND vector_dims(p_embedding) <> 1024 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: embedding dimension mismatch — expected 1024, got %',
            vector_dims(p_embedding);
    END IF;

    -- Q5: compute content_hash to detect upcoming bitemporal close (dedup observability)
    -- Replicates GENERATED ALWAYS AS formula from agent_lesson.content_hash column.
    _content_hash := MD5(
        COALESCE(p_role, '')        || '|' ||
        COALESCE(p_topic, '')       || '|' ||
        COALESCE(p_commit_sha, COALESCE(p_artifact_hash, ''))
    );

    SELECT COUNT(*)::INT INTO _prior_count
    FROM pgmnemo.agent_lesson
    WHERE content_hash = _content_hash
      AND t_valid_to   = 'infinity'::TIMESTAMPTZ;

    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance, embedding,
        commit_sha, artifact_hash, metadata, verified_at
    ) VALUES (
        p_role, p_project_id, p_topic, p_lesson_text, p_importance, p_embedding,
        p_commit_sha, p_artifact_hash, p_metadata,
        CASE WHEN p_commit_sha IS NOT NULL OR p_artifact_hash IS NOT NULL
             THEN NOW() ELSE NULL END
    ) RETURNING id INTO new_id;

    -- Q5: RAISE NOTICE if bitemporal close+create fired (trigger trg_agent_lesson_bitemporal_close)
    IF _prior_count > 0 THEN
        RAISE NOTICE 'pgmnemo.ingest: bitemporal close+create fired — closed % prior version(s) '
                     '(content_hash=%). New lesson_id=%. '
                     'Prior row(s) now have t_valid_to=NOW().',
                     _prior_count, _content_hash, new_id;
    END IF;

    RETURN new_id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.ingest(TEXT, INT, TEXT, TEXT, SMALLINT, vector, TEXT, TEXT, JSONB) IS
    'Validated public write API (v0.6.0 + Q5). '
    'Q5: RAISE NOTICE when bitemporal close+create fires (dedup observability). '
    'Caller receives NOTICE "bitemporal close+create fired — closed N prior version(s)". '
    'On idempotent re-run (same args): NOTICE fires again if prior row was already closed+recreated. '
    'Truncates p_lesson_text to pgmnemo.max_query_text_chars (default 2000) with RAISE NOTICE. '
    'R5 (v0.5.0).';

-- ─── R9: recall hit-count metric view ────────────────────────────────────────
-- RFC R9 (deferred from v0.4.1). Requires track_functions = 'pl' or
-- 'all' in postgresql.conf; rows only appear after the first call post-reset.

CREATE OR REPLACE VIEW pgmnemo.recall_stats AS
SELECT
    n.nspname   AS schema,
    p.proname   AS function_name,
    s.calls,
    s.total_time,
    s.self_time,
    NOW()       AS observed_at
FROM pg_stat_user_functions s
JOIN pg_proc      p ON p.oid = s.funcid
JOIN pg_namespace n ON n.oid = p.pronamespace
WHERE n.nspname = 'pgmnemo'
  AND p.proname IN ('recall_lessons', 'recall_hybrid', 'ingest');

COMMENT ON VIEW pgmnemo.recall_stats IS
    'Observability view for recall/ingest call counts (R9, v0.6.0). '
    'Requires track_functions = ''pl'' or ''all'' in postgresql.conf. '
    'Rows appear only after first call following a pg_stat_reset().';

-- =============================================================================
-- v0.7.0 additions (outcome-learning loop + footgun remediation)
-- =============================================================================

-- pgmnemo--0.6.3--0.7.0.sql
-- Incremental upgrade: v0.6.3 → v0.7.0
--
-- Theme: Outcome-learning loop + footgun remediation
--
-- A) Schema: confidence REAL, success_count INT, fail_count INT,
--            last_outcome TEXT, last_outcome_at TIMESTAMPTZ on agent_lesson
-- B) pgmnemo.reinforce(lesson_id, outcome) — asymmetric confidence update
-- C) recall_lessons() + recall_hybrid() — confidence in scoring + output
-- D) Footgun guard: RAISE NOTICE when embedding missing (recall_lessons vector-only path)
-- E) match_confidence [0,1] interpretable recall-match score in output
-- F) Ingestion guards: min-length reject, token-repetition reject, embedding dedup warn+return
-- G) stats() extension: confidence distribution columns (19 cols total, was 14)
--
-- Backward compatibility:
--   Named-column callers of recall_lessons/recall_hybrid: unaffected.
--   Positional callers: re-audit for new trailing cols (confidence, match_confidence).
--   ingest() signature unchanged (9 params).
--   stats() gains 5 columns -- named-column callers unaffected.
--
-- SPDX-License-Identifier: Apache-2.0

-- =============================================================================
-- A) Schema additions (idempotent via ADD COLUMN IF NOT EXISTS)
-- =============================================================================

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS confidence      REAL        NOT NULL DEFAULT 0.5
        CONSTRAINT ck_agent_lesson_confidence CHECK (confidence BETWEEN 0.0 AND 1.0),
    ADD COLUMN IF NOT EXISTS success_count   INT         NOT NULL DEFAULT 0,
    ADD COLUMN IF NOT EXISTS fail_count      INT         NOT NULL DEFAULT 0,
    ADD COLUMN IF NOT EXISTS last_outcome    TEXT,
    ADD COLUMN IF NOT EXISTS last_outcome_at TIMESTAMPTZ;

-- Partial index for monitoring at-risk (low-confidence) lessons
CREATE INDEX IF NOT EXISTS ix_pgmnemo_lesson_confidence_low
    ON pgmnemo.agent_lesson (confidence ASC)
    WHERE is_active AND confidence < 0.3;

COMMENT ON COLUMN pgmnemo.agent_lesson.confidence IS
    'Outcome-track-record confidence score [0.0, 1.0]. '
    'Default 0.5 (cold-start neutral). '
    'Updated by pgmnemo.reinforce(): success +pgmnemo.reinforce_success_delta (default 0.02), '
    'failure -pgmnemo.reinforce_fail_delta (default 0.12), neutral no-op. '
    'Clamped to [0.0, 1.0] by CHECK constraint + reinforce() logic. '
    'Defaults base-rate-adjusted for 83.5% base success rate.';

COMMENT ON COLUMN pgmnemo.agent_lesson.success_count IS
    'Cumulative count of successful recall outcomes recorded via reinforce().';

COMMENT ON COLUMN pgmnemo.agent_lesson.fail_count IS
    'Cumulative count of failure outcomes recorded via reinforce().';

COMMENT ON COLUMN pgmnemo.agent_lesson.last_outcome IS
    'Most recent outcome string from reinforce(): success | failure (neutral is a no-op).';

COMMENT ON COLUMN pgmnemo.agent_lesson.last_outcome_at IS
    'Timestamp of the most recent reinforce() call that changed this row (success or failure only).';

-- =============================================================================
-- B) pgmnemo.reinforce() -- GUC-configurable confidence update (v0.9.3/D1)
--    success: +pgmnemo.reinforce_success_delta (default 0.02)
--    failure: -pgmnemo.reinforce_fail_delta    (default 0.12)
--    neutral: no-op (no write)
--    Unknown outcome: RAISE EXCEPTION (exact case required)
--    Returns new confidence value (REAL).
--    Row-locked via SELECT ... FOR UPDATE for concurrent-safe update.
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.reinforce(
    p_lesson_id BIGINT,
    p_outcome   TEXT
)
RETURNS REAL
LANGUAGE plpgsql
AS $func$
#variable_conflict use_column
DECLARE
    _row           pgmnemo.agent_lesson%ROWTYPE;
    _new_conf      REAL;
    _success_delta DOUBLE PRECISION;
    _fail_delta    DOUBLE PRECISION;
BEGIN
    -- D1: read reinforce deltas from GUC (base-rate-adjusted defaults)
    BEGIN
        _success_delta := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.reinforce_success_delta', TRUE), '')::DOUBLE PRECISION,
            0.02)));
    EXCEPTION WHEN OTHERS THEN _success_delta := 0.02;
    END;

    BEGIN
        _fail_delta := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.reinforce_fail_delta', TRUE), '')::DOUBLE PRECISION,
            0.12)));
    EXCEPTION WHEN OTHERS THEN _fail_delta := 0.12;
    END;

    SELECT * INTO _row
    FROM pgmnemo.agent_lesson
    WHERE id = p_lesson_id
    FOR UPDATE;

    IF NOT FOUND THEN
        RAISE EXCEPTION 'pgmnemo.reinforce: lesson_id % not found', p_lesson_id;
    END IF;

    CASE p_outcome
        WHEN 'success' THEN
            _new_conf := LEAST(1.0, _row.confidence + _success_delta::REAL);
            UPDATE pgmnemo.agent_lesson
            SET confidence      = _new_conf,
                success_count   = _row.success_count + 1,
                last_outcome    = 'success',
                last_outcome_at = NOW()
            WHERE id = p_lesson_id;

        WHEN 'failure' THEN
            _new_conf := GREATEST(0.0, _row.confidence - _fail_delta::REAL);
            UPDATE pgmnemo.agent_lesson
            SET confidence      = _new_conf,
                fail_count      = _row.fail_count + 1,
                last_outcome    = 'failure',
                last_outcome_at = NOW()
            WHERE id = p_lesson_id;

        WHEN 'neutral' THEN
            _new_conf := _row.confidence;

        ELSE
            RAISE EXCEPTION
                'pgmnemo.reinforce: unknown outcome ''%'' -- expected ''success'', ''failure'', or ''neutral''',
                p_outcome;
    END CASE;

    RETURN _new_conf;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reinforce(BIGINT, TEXT) IS
    'Outcome-learning update (v0.9.3/D1): adjusts confidence for lesson p_lesson_id. '
    'Exact case required: ''success'' | ''failure'' | ''neutral''. '
    'success: confidence += pgmnemo.reinforce_success_delta (default 0.02, clamped [0.001,0.5]). '
    'failure: confidence -= pgmnemo.reinforce_fail_delta    (default 0.12, clamped [0.001,0.5]). '
    'neutral: no-op -- returns current confidence without any write. '
    'Unknown outcome string: RAISE EXCEPTION. '
    'Row-locked (SELECT ... FOR UPDATE) for concurrent-safe update on hot lessons. '
    'Defaults base-rate-adjusted for 83.5% success workload.';

-- =============================================================================
-- B2) pgmnemo.reinforce(BIGINT[], TEXT) — batch form (v0.9.3/D1, was v0.7.1)
--     Skips missing lesson_ids silently (no RAISE). Returns count updated.
--     Unknown outcome: RAISE EXCEPTION (caller error).
--     Empty/NULL array: returns 0.
--     Both scalar and batch read GUC deltas (base-rate-adjusted defaults).
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.reinforce(
    p_lesson_ids BIGINT[],
    p_outcome    TEXT
)
RETURNS INT
LANGUAGE plpgsql
AS $func$
DECLARE
    _id            BIGINT;
    _row           pgmnemo.agent_lesson%ROWTYPE;
    _new_conf      REAL;
    _updated       INT := 0;
    _success_delta DOUBLE PRECISION;
    _fail_delta    DOUBLE PRECISION;
BEGIN
    -- Validate outcome up-front so the caller gets a clear error on bad input.
    IF p_outcome NOT IN ('success', 'failure', 'neutral') THEN
        RAISE EXCEPTION
            'pgmnemo.reinforce: unknown outcome ''%'' -- expected ''success'', ''failure'', or ''neutral''',
            p_outcome;
    END IF;

    IF p_lesson_ids IS NULL OR array_length(p_lesson_ids, 1) IS NULL THEN
        RETURN 0;
    END IF;

    -- D1: read reinforce deltas from GUC (base-rate-adjusted defaults)
    BEGIN
        _success_delta := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.reinforce_success_delta', TRUE), '')::DOUBLE PRECISION,
            0.02)));
    EXCEPTION WHEN OTHERS THEN _success_delta := 0.02;
    END;

    BEGIN
        _fail_delta := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.reinforce_fail_delta', TRUE), '')::DOUBLE PRECISION,
            0.12)));
    EXCEPTION WHEN OTHERS THEN _fail_delta := 0.12;
    END;

    FOREACH _id IN ARRAY p_lesson_ids LOOP
        SELECT * INTO _row
        FROM pgmnemo.agent_lesson
        WHERE id = _id
        FOR UPDATE;

        IF NOT FOUND THEN
            CONTINUE;  -- skip missing; no RAISE (bitemporal supersession / TTL normal)
        END IF;

        CASE p_outcome
            WHEN 'success' THEN
                _new_conf := LEAST(1.0, _row.confidence + _success_delta::REAL);
                UPDATE pgmnemo.agent_lesson
                SET confidence      = _new_conf,
                    success_count   = _row.success_count + 1,
                    last_outcome    = 'success',
                    last_outcome_at = NOW()
                WHERE id = _id;
                _updated := _updated + 1;

            WHEN 'failure' THEN
                _new_conf := GREATEST(0.0, _row.confidence - _fail_delta::REAL);
                UPDATE pgmnemo.agent_lesson
                SET confidence      = _new_conf,
                    fail_count      = _row.fail_count + 1,
                    last_outcome    = 'failure',
                    last_outcome_at = NOW()
                WHERE id = _id;
                _updated := _updated + 1;

            WHEN 'neutral' THEN
                NULL;  -- no-op; does not increment _updated
        END CASE;
    END LOOP;

    RETURN _updated;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reinforce(BIGINT[], TEXT) IS
    'Batch confidence update v0.9.3/D1. Iterates p_lesson_ids; skips missing IDs silently (no RAISE). '
    'Returns count of rows actually updated (neutral outcome does not increment count). '
    'Unknown outcome string raises RAISE EXCEPTION (caller programming error). '
    'Empty or NULL array returns 0. '
    'success: +pgmnemo.reinforce_success_delta (default 0.02), failure: -pgmnemo.reinforce_fail_delta (default 0.12). '
    'Scalar form reinforce(BIGINT, TEXT) unchanged (same GUC reads).';

-- =============================================================================
-- C + D + E) recall_hybrid() v0.7.1
-- Return-type changes (new trailing cols) require DROP + CREATE.
-- New output columns appended at end: confidence REAL, match_confidence REAL
-- Scoring change: aux term = 0.025*(imp/5) + 0.025*confidence + 0.05*recency + 0.05*prov
-- match_confidence = LEAST(1.0, GREATEST(0.0, v_score))::REAL  [v0.7.1: vec_score not final_score/1.5]
-- =============================================================================

DROP FUNCTION IF EXISTS pgmnemo.recall_hybrid(
    vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT
);

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;  -- v0.8.1 F2: cardinal raw score blend
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);  -- same order as max RRF per signal

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    raw_candidates AS (
        SELECT
            al.id,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at,
            al.confidence,
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_vec_score,
            -- v0.8.1 F3: topic in BM25 — setweight(topic, 'A') || lesson_tsv
            CASE
                WHEN _has_text AND (al.lesson_tsv @@ _tsquery
                     OR to_tsvector('english', COALESCE(al.topic, '')) @@ _tsquery)
                THEN ts_rank_cd(
                    setweight(to_tsvector('english', COALESCE(al.topic, '')), 'A') || al.lesson_tsv,
                    _tsquery, 32)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (
              (_has_vec  AND al.embedding   IS NOT NULL)
           OR (_has_text AND (al.lesson_tsv @@ _tsquery
               OR to_tsvector('english', COALESCE(al.topic, '')) @@ _tsquery))
          )
    ),
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST)   AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM raw_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend (absolute match strength survives)
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
            -- v0.8.1 F1: graph is multiplicative re-rank (tie-breaker, not driver)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    )
    SELECT
        f.id                   AS lesson_id,
        f.final_score          AS score,
        f.v_score              AS vec_score,
        f.b_score              AS bm25_score,
        f.rrf_sparse           AS rrf_score,
        f.role,
        f.project_id,
        f.topic,
        f.lesson_text,
        f.importance,
        f.metadata,
        f.commit_sha,
        f.artifact_hash,
        f.verified_at,
        f.created_at,
        f.confidence::REAL,
        -- BUG-1 FIX (v0.7.1): use vec_score (cosine, [0,1]) not final_score/1.5.
        -- final_score is RRF-scale (~0.008-0.05); dividing by 1.5 produced ~0.005.
        -- vec_score is cosine similarity, already in [0,1] by pgvector guarantee.
        -- On text-only path (query_embedding IS NULL), vec_score = 0.0.
        LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
    FROM final f
    ORDER BY f.final_score DESC
    LIMIT k;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT) IS
    'Hybrid recall v0.7.1 -- match_confidence formula corrected (BUG-1), graph_proximity note added. '
    'RRF (Reciprocal Rank Fusion, Cormack 2009): combines vector + BM25 ranks. '
    'Scoring: rrf_sparse + _aux_scale*(0.025*imp/5 + 0.025*conf + 0.05*recency + 0.05*prov) + delta*graph. '
    'confidence: per-lesson outcome-track-record [0,1] from reinforce(). '
    'match_confidence: vec_score (cosine similarity, [0,1]). On text-only path (NULL embedding) = 0.0. '
    'graph_proximity contributes only when mem_edge is populated; with no edges the graph term is 0 (correct, not a bug). '
    'D-footgun: RAISE NOTICE when query_embedding IS NULL. '
    '17 output columns (15 existing + confidence REAL, match_confidence REAL).';

-- =============================================================================
-- C + D + E) recall_lessons() v0.7.0
-- Return-type changes (new trailing cols) require DROP + CREATE.
-- New output columns appended at end: confidence REAL, match_confidence REAL
-- Scoring change (vector-only): 0.2*(imp/5) => 0.15*(imp/5) + 0.15*confidence
-- D footgun: RAISE NOTICE when query_embedding IS NULL and _has_text
-- match_confidence = LEAST(1.0, GREATEST(0.0, score / 1.5))::REAL
-- =============================================================================

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(
    vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ
);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT         DEFAULT 10,
    role_filter       TEXT        DEFAULT NULL,
    project_id_filter INT         DEFAULT NULL,
    query_text        TEXT        DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
BEGIN
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT, 2000);
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars. Original: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    _has_vec  := query_embedding IS NOT NULL;
    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _disable_hybrid := FALSE;
    END;

    IF NOT _disable_hybrid AND _has_vec AND _has_text THEN
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        RETURN QUERY
        SELECT
            h.lesson_id, h.score, h.role, h.project_id, h.topic, h.lesson_text,
            h.importance, h.metadata, h.commit_sha, h.artifact_hash,
            h.verified_at, h.created_at,
            h.vec_score, h.bm25_score, h.rrf_score,
            h.confidence, h.match_confidence
        FROM pgmnemo.recall_hybrid(
            query_embedding, _query_text, k,
            role_filter, project_id_filter, 0.4, 0.4, 60
        ) h;
        RETURN;
    END IF;

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05);
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION, 1.0)));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', _query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', _query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id          AS cand_id,
            al.role        AS cand_role,
            al.project_id  AS cand_project_id,
            al.topic       AS cand_topic,
            al.lesson_text AS cand_lesson_text,
            al.importance  AS cand_importance,
            al.metadata    AS cand_metadata,
            al.commit_sha  AS cand_commit_sha,
            al.artifact_hash AS cand_artifact_hash,
            al.verified_at AS cand_verified_at,
            al.created_at  AS cand_created_at,
            al.confidence  AS cand_confidence,
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score_raw,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score_raw
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
          AND (as_of_ts IS NULL
               OR (al.t_valid_from <= as_of_ts AND al.t_valid_to > as_of_ts))
          AND (al.embedding IS NOT NULL OR _has_text)
        ORDER BY al.embedding <=> query_embedding
        LIMIT GREATEST(k * 5, 50)
    ),
    anchors AS (
        SELECT cand_id FROM candidates ORDER BY vec_score_raw DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT cand_id, 0, cand_id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT reached_id AS gp_lesson_id,
               MAX(1.0 - depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk WHERE depth > 0 GROUP BY reached_id
    )
    SELECT
        c.cand_id               AS lesson_id,
        (
            0.5  * c.vec_score_raw
          + 0.15 * (c.cand_importance::DOUBLE PRECISION / 5.0)
          + 0.15 * c.cand_confidence::DOUBLE PRECISION
          + _gamma * GREATEST(0.0, 1.0 - LEAST(
                EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0))
          + 0.1 * (CASE
                WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
                ELSE 0.0 END)
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        )                       AS score,
        c.cand_role             AS role,
        c.cand_project_id       AS project_id,
        c.cand_topic            AS topic,
        c.cand_lesson_text      AS lesson_text,
        c.cand_importance       AS importance,
        c.cand_metadata         AS metadata,
        c.cand_commit_sha       AS commit_sha,
        c.cand_artifact_hash    AS artifact_hash,
        c.cand_verified_at      AS verified_at,
        c.cand_created_at       AS created_at,
        c.vec_score_raw         AS vec_score,
        NULL::DOUBLE PRECISION  AS bm25_score,
        NULL::DOUBLE PRECISION  AS rrf_score,
        c.cand_confidence::REAL AS confidence,
        LEAST(1.0, GREATEST(0.0,
            (
                0.5  * c.vec_score_raw
              + 0.15 * (c.cand_importance::DOUBLE PRECISION / 5.0)
              + 0.15 * c.cand_confidence::DOUBLE PRECISION
              + _gamma * GREATEST(0.0, 1.0 - LEAST(
                    EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0))
              + 0.1 * (CASE
                    WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                    WHEN c.cand_commit_sha IS NOT NULL THEN 0.5 ELSE 0.0 END)
              + _graph_weight * COALESCE(gp.proximity, 0.0)
            ) / 1.5
        ))::REAL                AS match_confidence
    FROM candidates c
    LEFT JOIN graph_proximity gp ON gp.gp_lesson_id = c.cand_id
    ORDER BY score DESC, c.cand_importance DESC, c.cand_created_at DESC
    LIMIT k;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ) IS
    'v0.7.0 -- confidence integration + footgun guard + match_confidence. '
    'Scoring (vector path): 0.5*vec + 0.15*imp/5 + 0.15*confidence + gamma*recency + 0.1*prov + delta*graph. '
    'confidence: outcome-track-record [0,1] from reinforce(). '
    'match_confidence: LEAST(1.0, GREATEST(0.0, score/1.5)) -- interpretable [0,1] quality indicator. '
    'D-footgun: RAISE NOTICE when query_embedding IS NULL and text-only fallback active. '
    '17 output columns (15 existing + confidence REAL, match_confidence REAL). '
    'Named-column callers unaffected; positional callers: re-audit for 2 new trailing cols.';

-- =============================================================================
-- F) Ingestion guards -- CREATE OR REPLACE (signature unchanged, 9 params)
-- F1: lesson_text < 20 chars -> RAISE EXCEPTION
-- F2: most-frequent token > 80% of all tokens -> RAISE EXCEPTION (repetitive content)
-- F3: cosine similarity > 0.98 to existing active lesson (same project_id) ->
--     RAISE WARNING + RETURN existing lesson_id (no new insert)
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.ingest(
    p_role          TEXT,
    p_project_id    INT,
    p_topic         TEXT,
    p_lesson_text   TEXT,
    p_importance    SMALLINT     DEFAULT 3,
    p_embedding     vector(1024) DEFAULT NULL,
    p_commit_sha    TEXT         DEFAULT NULL,
    p_artifact_hash TEXT         DEFAULT NULL,
    p_metadata      JSONB        DEFAULT '{}'::jsonb
) RETURNS BIGINT
LANGUAGE plpgsql AS $func$
DECLARE
    new_id             BIGINT;
    _content_hash      TEXT;
    _prior_count       INT;
    _dedup_id          BIGINT;
    _dedup_sim         DOUBLE PRECISION;
    _tokens            TEXT[];
    _token             TEXT;
    _token_counts      JSONB;
    _max_freq          INT;
    _total_tokens      INT;
    _trimmed_text      TEXT;
BEGIN
    -- F1: minimum length guard (fires BEFORE provenance gate trigger)
    IF p_lesson_text IS NULL OR length(trim(p_lesson_text)) < 20 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: lesson_text too short (min 20 chars)';
    END IF;

    -- F2: token-frequency repetition guard
    _trimmed_text := trim(p_lesson_text);
    _tokens := regexp_split_to_array(
        regexp_replace(_trimmed_text, '\s+', ' ', 'g'), ' ');
    _total_tokens := array_length(_tokens, 1);

    IF _total_tokens > 0 THEN
        _token_counts := '{}'::JSONB;
        FOREACH _token IN ARRAY _tokens LOOP
            _token_counts := jsonb_set(
                _token_counts,
                ARRAY[_token],
                to_jsonb(COALESCE((_token_counts->>_token)::INT, 0) + 1)
            );
        END LOOP;

        SELECT MAX(value::INT)
        INTO _max_freq
        FROM jsonb_each_text(_token_counts);

        IF _max_freq::DOUBLE PRECISION / _total_tokens::DOUBLE PRECISION > 0.8 THEN
            RAISE EXCEPTION 'pgmnemo.ingest: lesson_text appears to be repetitive content';
        END IF;
    END IF;

    -- Embedding dimension guard
    IF p_embedding IS NOT NULL AND vector_dims(p_embedding) <> 1024 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: embedding dimension mismatch -- expected 1024, got %',
            vector_dims(p_embedding);
    END IF;

    -- F3: near-duplicate embedding guard (cosine > 0.98, same project_id)
    IF p_embedding IS NOT NULL THEN
        SELECT id, (1.0 - (embedding <=> p_embedding))
        INTO _dedup_id, _dedup_sim
        FROM pgmnemo.agent_lesson
        WHERE is_active
          AND t_valid_to = 'infinity'::TIMESTAMPTZ
          AND embedding IS NOT NULL
          AND project_id = p_project_id
          AND (1.0 - (embedding <=> p_embedding)) > 0.98
        ORDER BY embedding <=> p_embedding
        LIMIT 1;

        IF FOUND THEN
            RAISE WARNING
                'pgmnemo.ingest: near-duplicate detected -- cosine similarity % > 0.98 '
                'to existing lesson_id=% (project_id=%). Returning existing lesson_id.',
                ROUND(_dedup_sim::NUMERIC, 4), _dedup_id, p_project_id;
            RETURN _dedup_id;
        END IF;
    END IF;

    -- Bitemporal dedup observability
    _content_hash := MD5(
        COALESCE(p_role, '')  || '|' ||
        COALESCE(p_topic, '') || '|' ||
        COALESCE(p_commit_sha, COALESCE(p_artifact_hash, ''))
    );

    SELECT COUNT(*)::INT INTO _prior_count
    FROM pgmnemo.agent_lesson
    WHERE content_hash = _content_hash
      AND t_valid_to   = 'infinity'::TIMESTAMPTZ;

    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance, embedding,
        commit_sha, artifact_hash, metadata, verified_at
    ) VALUES (
        p_role, p_project_id, p_topic, p_lesson_text, p_importance, p_embedding,
        p_commit_sha, p_artifact_hash, p_metadata,
        -- v0.9.0 #2: verified_at = NOW() for ALL lessons passing quality gates.
        -- NULL-embedding lessons are text-only (BM25 path); must not be ghost-excluded.
        NOW()
    ) RETURNING id INTO new_id;

    IF _prior_count > 0 THEN
        RAISE NOTICE
            'pgmnemo.ingest: bitemporal close+create fired -- closed % prior version(s) '
            '(content_hash=%). New lesson_id=%.',
            _prior_count, _content_hash, new_id;
    END IF;

    RETURN new_id;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.ingest(TEXT, INT, TEXT, TEXT, SMALLINT, vector, TEXT, TEXT, JSONB) IS
    'Validated public write API v0.9.0. '
    'F1 (min-length): RAISE EXCEPTION when lesson_text < 20 chars. '
    'F2 (repetition): RAISE EXCEPTION when most-frequent token > 80%% of all tokens. '
    'F3 (dedup-warn): RAISE WARNING + RETURN existing lesson_id when cosine_sim > 0.98. '
    'v0.9.0: verified_at = NOW() for all lessons passing quality gates (NULL-embedding != ghost). '
    'Provenance tier (commit_sha, artifact_hash) still contributes to ranking aux score.';

-- =============================================================================
-- G) stats() v0.7.0 -- confidence distribution columns
-- Return-type change requires DROP + CREATE.
-- 5 new columns: confidence_mean, confidence_p10, confidence_p50, confidence_p90,
--                confidence_below_threshold_count (confidence < 0.3)
-- =============================================================================

DROP FUNCTION IF EXISTS pgmnemo.stats();

CREATE OR REPLACE FUNCTION pgmnemo.stats()
RETURNS TABLE (
    version                          TEXT,
    lesson_count                     BIGINT,
    embedded_count                   BIGINT,
    embedding_coverage_pct           DOUBLE PRECISION,
    tsv_coverage_pct                 DOUBLE PRECISION,
    mem_edge_count                   BIGINT,
    recency_weight                   DOUBLE PRECISION,
    ef_search                        INT,
    importance_weight                DOUBLE PRECISION,
    hybrid_enabled                   BOOLEAN,
    recall_hybrid_available          BOOLEAN,
    oldest_lesson_age_days           INT,
    orphan_count                     BIGINT,
    ghost_count                      BIGINT,
    confidence_mean                  REAL,
    confidence_p10                   REAL,
    confidence_p50                   REAL,
    confidence_p90                   REAL,
    confidence_below_threshold_count INT
)
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $func$
    SELECT
        pgmnemo.version()                                                          AS version,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.agent_lesson)                        AS lesson_count,
        (SELECT COUNT(*)::BIGINT
         FROM pgmnemo.agent_lesson WHERE embedding IS NOT NULL)                    AS embedded_count,
        (SELECT CASE WHEN COUNT(*) > 0
                     THEN ROUND(100.0 *
                          SUM(CASE WHEN embedding IS NOT NULL THEN 1 ELSE 0 END)::NUMERIC
                          / COUNT(*), 2)::DOUBLE PRECISION
                     ELSE 0.0 END
         FROM pgmnemo.agent_lesson)                                                AS embedding_coverage_pct,
        (SELECT CASE WHEN COUNT(*) > 0
                     THEN ROUND(100.0 *
                          SUM(CASE WHEN lesson_tsv IS NOT NULL THEN 1 ELSE 0 END)::NUMERIC
                          / COUNT(*), 2)::DOUBLE PRECISION
                     ELSE 0.0 END
         FROM pgmnemo.agent_lesson)                                                AS tsv_coverage_pct,
        (SELECT COUNT(*)::BIGINT FROM pgmnemo.mem_edge)                            AS mem_edge_count,
        COALESCE(NULLIF(current_setting('pgmnemo.recency_weight',  TRUE), '')::DOUBLE PRECISION,
                 0.05)                                                             AS recency_weight,
        COALESCE(NULLIF(current_setting('pgmnemo.ef_search',       TRUE), '')::INT,
                 100)                                                              AS ef_search,
        COALESCE(NULLIF(current_setting('pgmnemo.importance_weight',TRUE), '')::DOUBLE PRECISION,
                 0.15)                                                             AS importance_weight,
        NOT COALESCE(current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN,
                     FALSE)                                                        AS hybrid_enabled,
        EXISTS (
            SELECT 1 FROM pg_proc p
            JOIN pg_namespace n ON n.oid = p.pronamespace
            WHERE n.nspname = 'pgmnemo' AND p.proname = 'recall_hybrid'
        )                                                                          AS recall_hybrid_available,
        (SELECT COALESCE(
                    EXTRACT(EPOCH FROM (NOW() - MIN(created_at))) / 86400.0, 0
                )::INT
         FROM pgmnemo.agent_lesson)                                                AS oldest_lesson_age_days,
        (SELECT COUNT(*)::BIGINT
         FROM pg_proc p
         JOIN pg_namespace n ON n.oid = p.pronamespace
         LEFT JOIN pg_depend d
             ON d.objid = p.oid AND d.deptype = 'e'
            AND d.refobjid = (SELECT oid FROM pg_extension WHERE extname = 'pgmnemo')
         WHERE n.nspname = 'pgmnemo'
           AND p.proname NOT LIKE '\_%' ESCAPE '\'
           AND d.objid IS NULL)                                                    AS orphan_count,
        (SELECT COUNT(*)::BIGINT
         FROM pgmnemo.agent_lesson
         WHERE verified_at IS NULL
           AND t_valid_to = 'infinity'::TIMESTAMPTZ)                               AS ghost_count,
        (SELECT COALESCE(AVG(confidence), 0.5)::REAL
         FROM pgmnemo.agent_lesson
         WHERE t_valid_to = 'infinity'::TIMESTAMPTZ)                               AS confidence_mean,
        (SELECT COALESCE(
                    PERCENTILE_CONT(0.10) WITHIN GROUP (ORDER BY confidence), 0.5
                )::REAL
         FROM pgmnemo.agent_lesson
         WHERE t_valid_to = 'infinity'::TIMESTAMPTZ)                               AS confidence_p10,
        (SELECT COALESCE(
                    PERCENTILE_CONT(0.50) WITHIN GROUP (ORDER BY confidence), 0.5
                )::REAL
         FROM pgmnemo.agent_lesson
         WHERE t_valid_to = 'infinity'::TIMESTAMPTZ)                               AS confidence_p50,
        (SELECT COALESCE(
                    PERCENTILE_CONT(0.90) WITHIN GROUP (ORDER BY confidence), 0.5
                )::REAL
         FROM pgmnemo.agent_lesson
         WHERE t_valid_to = 'infinity'::TIMESTAMPTZ)                               AS confidence_p90,
        (SELECT COUNT(*)::INT
         FROM pgmnemo.agent_lesson
         WHERE t_valid_to = 'infinity'::TIMESTAMPTZ
           AND confidence < 0.3)                                                   AS confidence_below_threshold_count;
$func$;

COMMENT ON FUNCTION pgmnemo.stats() IS
    'v0.7.0 diagnostic health-check (19 columns, was 14). '
    'New columns: confidence_mean REAL, confidence_p10 REAL, confidence_p50 REAL, '
    'confidence_p90 REAL, confidence_below_threshold_count INT (lessons with confidence < 0.3). '
    'ghost_count: active lessons (t_valid_to=infinity) without provenance. '
    'orphan_count: pgmnemo-schema functions not owned by the extension. '
    'Single-row; <100ms on N=10k corpus.';

-- ─────────────────────────────────────────────────────────────────────────────
-- pgmnemo 0.7.2 → 0.8.0 delta (fresh-install inclusion)
-- ─────────────────────────────────────────────────────────────────────────────

-- S1: Schema additions — source_type + embedding_at
-- ─────────────────────────────────────────────────────────────────────────────

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS source_type TEXT
        DEFAULT 'auto_captured'
        CONSTRAINT ck_agent_lesson_source_type
            CHECK (source_type IN ('agent_authored', 'auto_captured', 'imported', 'system'));

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS embedding_at TIMESTAMPTZ;

-- Backfill embedding_at for rows that already have embeddings.
-- Use updated_at as a reasonable proxy for when the embedding was last set.
UPDATE pgmnemo.agent_lesson
SET embedding_at = updated_at
WHERE embedding IS NOT NULL
  AND embedding_at IS NULL;

COMMENT ON COLUMN pgmnemo.agent_lesson.source_type IS
    'Origin classification for this lesson. '
    'agent_authored: explicitly written by an agent. '
    'auto_captured: automatically generated from agent output (default). '
    'imported: loaded from an external system. '
    'system: created by pgmnemo internal processes.';

COMMENT ON COLUMN pgmnemo.agent_lesson.embedding_at IS
    'Timestamp of the most recent embedding update via reembed() or reembed_batch(). '
    'NULL for rows embedded before 0.8.0 (backfilled to updated_at on upgrade). '
    'Updated by reembed() and reembed_batch(); NOT updated by ingest() (use embedding_at '
    'IS NULL AND embedding IS NOT NULL to identify rows needing a refresh timestamp).';

-- ─────────────────────────────────────────────────────────────────────────────
-- S2: navigate_locate() — Budget-bounded LOCATE
-- ─────────────────────────────────────────────────────────────────────────────
--
-- Algorithm:
--   1. raw_candidates: union of vector+BM25 candidates with JSONB predicate pushdown
--   2. rrf_ranked:     sparse-safe RRF ranks (Cormack 2009 — same as recall_hybrid)
--   3. scored:         rrf_sparse + aux(importance, recency, provenance) + graph_proximity
--   4. anchors/graph_walk/graph_proximity: BFS on causal+temporal edges (top-5 anchors)
--   5. budget_window:  SUM(length(lesson_text)) window; stop when cumulative > budget
--   6. Output:         id, score, tokens_consumed (cumulative chars up to this row),
--                      navigation_path ('vector'|'bm25'|'jsonb_gate')
--
-- navigation_path:
--   'jsonb_gate' when jsonb_filter is non-null (JSONB predicate gated the candidate set)
--   'vector'     when vec_rank <= effective_bm25_rank (vector was dominant signal)
--   'bm25'       otherwise (BM25 was dominant signal)
--
-- Budget semantics: the first row is always returned even if its length exceeds budget.
-- tokens_consumed is the cumulative char count INCLUDING the current row.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.navigate_locate(
    query_embedding   vector(1024),
    query_text        TEXT,
    token_budget_chars INT              DEFAULT 2000,
    jsonb_filter      JSONB             DEFAULT NULL,
    project_id_filter INT               DEFAULT NULL
)
RETURNS TABLE (
    id              BIGINT,
    preview         TEXT,
    score           FLOAT8,
    tokens_consumed INT,
    navigation_path TEXT
)
LANGUAGE plpgsql
VOLATILE
PARALLEL UNSAFE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 2;  -- proximity scoring: 2-hop sufficient; deeper = navigate_expand
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _vec_weight         CONSTANT DOUBLE PRECISION := 0.4;
    _bm25_weight        CONSTANT DOUBLE PRECISION := 0.4;
    _raw_blend_weight   DOUBLE PRECISION;  -- v0.8.1 F2: cardinal raw score blend
    _track_recency      BOOLEAN;           -- v0.9.5: recall-recency stamping
BEGIN
    -- v0.9.5: read track_recall_recency GUC (default TRUE)
    BEGIN
        _track_recency := COALESCE(
            NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
            TRUE);
    EXCEPTION WHEN OTHERS THEN _track_recency := TRUE;
    END;

    -- Validate: need at least one retrieval signal
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;
    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION 'pgmnemo.navigate_locate: both query_embedding and query_text are NULL/empty';
    END IF;

    _rrf_k_f := 60.0;
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);  -- same order as max RRF per signal

    -- ef_search GUC
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    -- include_unverified GUC
    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    -- as_of_ts GUC (bitemporal point-in-time filter)
    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN
        _as_of_ts := NULL;
    END;

    -- graph_proximity_weight GUC
    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    -- Parse query_text → tsquery
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    -- Step 1: union candidates; JSONB predicate pushdown when jsonb_filter non-null
    raw_candidates AS (
        SELECT
            al.id,
            al.importance,
            al.created_at,
            al.commit_sha,
            al.verified_at,
            LEAST(length(al.lesson_text), 50)                                        AS text_len,
            -- vector score
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_vec_score,
            -- P1-D fix: use stored generated columns (topic_tsv, lesson_tsv) — GIN-indexed.
            -- Was: inline to_tsvector('english', COALESCE(topic,'')) — O(n) seq-scan per row.
            -- topic_tsv GENERATED ALWAYS AS (to_tsvector('english', coalesce(topic,''))) STORED
            -- pgmnemo_agent_lesson_topic_tsv_idx GIN index on topic_tsv.
            CASE
                WHEN _has_text AND (al.lesson_tsv @@ _tsquery
                     OR al.topic_tsv @@ _tsquery)
                THEN ts_rank_cd(
                    setweight(al.topic_tsv, 'A') || al.lesson_tsv,
                    _tsquery, 32)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          -- JSONB predicate pushdown — uses GIN index on metadata when non-null
          AND (jsonb_filter IS NULL OR al.metadata @> jsonb_filter)
          -- project_id_filter — uses B-tree index pgmnemo_agent_lesson_project_idx
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
          -- bitemporal: active rows only (unless as_of_ts set)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
          -- union: matched by vector OR BM25 (including topic match)
          AND (
              (_has_vec  AND al.embedding  IS NOT NULL)
           OR (_has_text AND (al.lesson_tsv @@ _tsquery
               OR al.topic_tsv @@ _tsquery))
          )
    ),
    -- Step 2: sparse-safe RRF ranks (Cormack 2009)
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                                          AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST)               AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                                       AS bm25_rank_sparse
        FROM raw_candidates
    ),
    -- Step 3: RRF score + aux tie-breaker (identical formula to recall_hybrid v0.6.2)
    scored AS (
        SELECT
            r.id,
            r.importance,
            r.created_at,
            r.commit_sha,
            r.verified_at,
            r.text_len,
            r.vec_rank,
            COALESCE(r.bm25_rank_sparse, r.n_candidates + 1) AS bm25_rank_eff,
            r.raw_vec_score,
            r.raw_bm25_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend (absolute match strength survives)
            (_vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + _bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                   r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 _vec_weight  * r.raw_vec_score
               + _bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    -- Step 4: top-5 anchors for graph BFS
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    -- Step 5: BFS on active edges from top-5 anchors — BIDIRECTIONAL
    -- v0.9.1 fix: traverses ALL relation_types (was edge_kind IN causal/temporal)
    -- Bidirectional: discovers both forward and backward graph neighbors
    graph_walk (anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1,
               CASE WHEN me.source_id = gw.reached_id
                    THEN me.target_id
                    ELSE me.source_id
               END
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON (
            me.source_id = gw.reached_id OR me.target_id = gw.reached_id
        )
        WHERE gw.depth < _max_depth
          -- v0.9.1 fix: handle both active-edge sentinels
          AND (me.valid_until IS NULL OR me.valid_until = 'infinity'::TIMESTAMPTZ)
    ),
    -- Step 6: proximity score from BFS depth
    graph_proximity AS (
        SELECT
            gw.reached_id AS lesson_id,
            MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw
        WHERE gw.depth > 0
        GROUP BY gw.reached_id
    ),
    -- Step 7: final score = (rrf_sparse + aux) * (1 + graph); safety cap at 200 rows
    -- v0.8.1 F1: graph is multiplicative re-rank (tie-breaker, not driver)
    final_ranked AS (
        SELECT
            s.id,
            s.text_len,
            s.vec_rank,
            s.bm25_rank_eff,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.05 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.05 * GREATEST(0.0,
                               1.0 - LEAST(
                                   EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0),
                                   1.0
                               )
                           )::DOUBLE PRECISION
                  + 0.05 * (CASE
                              WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                              WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                              ELSE                                                             0.0
                            END)::DOUBLE PRECISION
                )
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
        ORDER BY (
                s.rrf_sparse
              + _aux_scale * (
                    0.05 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.05 * GREATEST(0.0,
                               1.0 - LEAST(
                                   EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0),
                                   1.0
                               )
                           )::DOUBLE PRECISION
                  + 0.05 * (CASE
                              WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                              WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                              ELSE                                                             0.0
                            END)::DOUBLE PRECISION
                )
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
            DESC
        LIMIT 200  -- safety cap: prevents unbounded result sets for large corpora
    ),
    -- Step 8: budget window — cumulative char sum over score-descending order
    budget_window AS (
        SELECT
            fr.id,
            fr.final_score,
            fr.text_len,
            fr.vec_rank,
            fr.bm25_rank_eff,
            SUM(fr.text_len) OVER (
                ORDER BY fr.final_score DESC, fr.id ASC
                ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
            ) AS cum_chars,
            ROW_NUMBER() OVER (ORDER BY fr.final_score DESC, fr.id ASC) AS rn
        FROM final_ranked fr
    ),
    -- v0.9.5: stamp recalled lessons via data-modifying CTE
    result_set AS MATERIALIZED (
        SELECT
            bw.id::BIGINT                                                                 AS id,
            left(al.lesson_text, 50)                                                      AS preview,
            bw.final_score::FLOAT8                                                        AS score,
            bw.cum_chars::INT                                                             AS tokens_consumed,
            -- navigation_path: jsonb_gate if filter was applied, else dominant retrieval signal
            CASE
                WHEN jsonb_filter IS NOT NULL THEN 'jsonb_gate'
                WHEN bw.vec_rank <= bw.bm25_rank_eff THEN 'vector'
                ELSE 'bm25'
            END                                                                           AS navigation_path
        FROM budget_window bw
        JOIN pgmnemo.agent_lesson al ON al.id = bw.id
        WHERE bw.rn = 1                                    -- always return first row
           OR (bw.cum_chars - bw.text_len) < token_budget_chars   -- include while under budget
        ORDER BY bw.final_score DESC, bw.id ASC
    ),
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count      = recall_count + 1
        WHERE _track_recency
          AND id = ANY(ARRAY(SELECT r.id FROM result_set r))
        RETURNING id
    )
    SELECT * FROM result_set;
END;
$$;

COMMENT ON FUNCTION pgmnemo.navigate_locate(vector, TEXT, INT, JSONB, INT) IS
    'Token-economy navigation LOCATE (v0.9.5). '
    'v0.9.5: VOLATILE (was STABLE PARALLEL SAFE); stamps last_recalled_at + recall_count '
    'on returned lessons via data-modifying CTE, gated by pgmnemo.track_recall_recency GUC (default TRUE). '
    'Ranks lessons using the same hybrid RRF+aux+graph formula as recall_hybrid. '
    'v0.9.1 fixes: (1) graph_walk now traverses ALL relation_types bidirectionally '
    '(was edge_kind IN causal/temporal — missed production edges with edge_kind=semantic); '
    '(2) raw_candidates uses stored topic_tsv column (GIN-indexed) instead of inline '
    'to_tsvector recompute — eliminates O(n) seq-scan on topic for BM25 match. '
    'Returns id/preview/score/tokens_consumed/navigation_path — preview is first ~50 chars. '
    'token_budget_chars: cumulative Unicode character (code-point) limit on delivered previews; '
    'first row always returned regardless of budget. '
    'jsonb_filter: WHERE metadata @> jsonb_filter pushed into candidate scan (uses GIN index). '
    'project_id_filter: scopes candidates to a single project (uses B-tree index). '
    'navigation_path: ''jsonb_gate'' when filter applied; ''vector'' when vec dominant; ''bm25'' otherwise. '
    'Combine with navigate_expand() to retrieve content for chosen IDs.';

-- ─────────────────────────────────────────────────────────────────────────────
-- S3: navigate_expand() — On-demand content + graph expansion
-- ─────────────────────────────────────────────────────────────────────────────
--
-- Returns full lesson_text for caller-chosen IDs.
-- If expand_fields non-empty: projects those keys from metadata JSONB into expand_detail.
-- If graph_expand_depth >= 1: recursively follows edges from input IDs,
--   only traversing edges with weight >= graph_expand_threshold.
--   relation_types: NULL = traverse ALL active edges; or pass specific relation_type values.
--   Bidirectional BFS: discovers both forward and backward relations.
--   Neighbour rows get navigation_path='graph_expand'; direct rows get 'content'.
--   Deduplication: input IDs always take priority (navigation_path='content').
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.navigate_expand(
    ids                    BIGINT[],
    expand_fields          TEXT[]           DEFAULT '{}',
    graph_expand_depth     INT              DEFAULT 1,
    graph_expand_threshold FLOAT            DEFAULT 0.5,
    relation_types         TEXT[]           DEFAULT NULL
)
RETURNS TABLE (
    id                      BIGINT,
    content                 TEXT,
    expand_detail           JSONB,
    graph_neighbor_ids      BIGINT[],
    graph_neighbor_previews TEXT[],
    tokens_consumed         INT,
    navigation_path         TEXT
)
LANGUAGE plpgsql
VOLATILE
PARALLEL UNSAFE
AS $$
DECLARE
    _track_recency BOOLEAN;
BEGIN
    -- v0.9.5: read track_recall_recency GUC
    BEGIN
        _track_recency := COALESCE(
            NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN, TRUE);
    EXCEPTION WHEN OTHERS THEN
        _track_recency := TRUE;
    END;

    IF ids IS NULL OR array_length(ids, 1) IS NULL THEN
        RETURN;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    -- Step 1: seed rows — the requested IDs
    seed_rows AS (
        SELECT
            al.id,
            al.lesson_text,
            al.metadata,
            CASE
                WHEN expand_fields IS NOT NULL AND array_length(expand_fields, 1) > 0
                THEN (
                    SELECT jsonb_object_agg(f, al.metadata->f)
                    FROM unnest(expand_fields) AS f
                    WHERE al.metadata ? f
                )
                ELSE NULL::JSONB
            END                                                            AS expand_detail,
            'content'::TEXT                                                AS navigation_path,
            0                                                              AS depth,
            ARRAY[al.id]                                                   AS path
        FROM pgmnemo.agent_lesson al
        WHERE al.id = ANY(ids)
          AND al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
    ),
    -- Step 2: BFS graph expansion — BIDIRECTIONAL, relation_type-gated, weight-gated
    graph_expand (node_id, lesson_text, metadata, depth, path) AS (
        -- Seed: input IDs
        SELECT
            sr.id,
            sr.lesson_text,
            sr.metadata,
            0,
            sr.path
        FROM seed_rows sr

        UNION ALL

        -- Recursive: traverse edges in BOTH directions, gated by relation_type + weight
        SELECT
            al.id,
            al.lesson_text,
            al.metadata,
            ge.depth + 1,
            ge.path || al.id
        FROM graph_expand ge
        JOIN pgmnemo.mem_edge me ON (
            me.source_id = ge.node_id OR me.target_id = ge.node_id
        )
        JOIN pgmnemo.agent_lesson al ON al.id = CASE
            WHEN me.source_id = ge.node_id THEN me.target_id
            ELSE me.source_id
        END
        WHERE graph_expand_depth >= 1
          AND ge.depth < graph_expand_depth
          -- B1 fix: filter by relation_type (the real discriminator)
          -- NULL relation_types = traverse ALL active edges (no type filter)
          AND (relation_types IS NULL OR me.relation_type = ANY(relation_types))
          -- Weight gate: configurable threshold (default 0.5)
          AND me.weight >= graph_expand_threshold::REAL
          -- B2 fix: handle both active-edge sentinel conventions
          AND (me.valid_until IS NULL OR me.valid_until = 'infinity'::TIMESTAMPTZ)
          -- Target lesson must be active
          AND al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          -- Cycle guard: prevent revisiting nodes already in path
          AND NOT (al.id = ANY(ge.path))
    ),
    -- Step 3: collect expanded rows (exclude IDs already in seed)
    expanded_rows AS (
        SELECT DISTINCT ON (ge.node_id)
            ge.node_id                                                         AS id,
            ge.lesson_text,
            ge.metadata,
            CASE
                WHEN expand_fields IS NOT NULL AND array_length(expand_fields, 1) > 0
                THEN (
                    SELECT jsonb_object_agg(f, ge.metadata->f)
                    FROM unnest(expand_fields) AS f
                    WHERE ge.metadata ? f
                )
                ELSE NULL::JSONB
            END                                                                AS expand_detail,
            'graph_expand'::TEXT                                               AS navigation_path
        FROM graph_expand ge
        WHERE ge.depth > 0                         -- only expansion rows, not seeds
          AND NOT (ge.node_id = ANY(ids))          -- exclude original input IDs
        ORDER BY ge.node_id, ge.depth ASC          -- prefer shallower depth
    ),
    -- Step 4b: deduplicated BFS neighbors per seed (all depths)
    distinct_neighbors AS (
        SELECT DISTINCT ON (ge.path[1], ge.node_id)
            ge.path[1]                                     AS seed_id,
            ge.node_id,
            ge.depth,
            left(ge.lesson_text, 50)                       AS neighbor_preview
        FROM graph_expand ge
        WHERE ge.depth > 0
          AND NOT (ge.node_id = ANY(ids))
        ORDER BY ge.path[1], ge.node_id, ge.depth ASC
    ),
    -- Step 4c: aggregate neighbors per seed — positional correspondence guaranteed
    neighbor_summary AS (
        SELECT
            dn.seed_id,
            array_agg(dn.node_id ORDER BY dn.depth, dn.node_id)          AS neighbor_ids,
            array_agg(dn.neighbor_preview ORDER BY dn.depth, dn.node_id) AS neighbor_previews
        FROM distinct_neighbors dn
        GROUP BY dn.seed_id
    ),
    -- Step 5: union seed + expanded, seed takes priority on id collision
    combined AS (
        SELECT sr.id,
               sr.lesson_text                                  AS content,
               sr.expand_detail,
               ns.neighbor_ids                                 AS graph_neighbor_ids,
               ns.neighbor_previews                            AS graph_neighbor_previews,
               sr.navigation_path
        FROM seed_rows sr
        LEFT JOIN neighbor_summary ns ON ns.seed_id = sr.id

        UNION ALL

        SELECT er.id,
               er.lesson_text                                  AS content,
               er.expand_detail,
               NULL::BIGINT[]                                  AS graph_neighbor_ids,
               NULL::TEXT[]                                     AS graph_neighbor_previews,
               er.navigation_path
        FROM expanded_rows er
    ),
    -- Step 6: attach cumulative tokens_consumed + v0.9.5 stamp
    result_set AS MATERIALIZED (
        SELECT
            c.id,
            c.content,
            c.expand_detail,
            c.graph_neighbor_ids,
            c.graph_neighbor_previews,
            SUM(length(c.content)) OVER (
                ORDER BY c.id ASC
                ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
            )::INT                                                 AS tokens_consumed,
            c.navigation_path
        FROM combined c
        ORDER BY c.id ASC
    ),
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count      = recall_count + 1
        WHERE _track_recency
          AND id = ANY(ARRAY(SELECT r.id FROM result_set r))
        RETURNING id
    )
    SELECT * FROM result_set;
END;
$$;

COMMENT ON FUNCTION pgmnemo.navigate_expand(BIGINT[], TEXT[], INT, FLOAT, TEXT[]) IS
    'Token-economy navigation EXPAND (v0.9.5). '
    'v0.9.5: VOLATILE (was STABLE PARALLEL SAFE); stamps last_recalled_at + recall_count '
    'on returned lessons via data-modifying CTE, gated by pgmnemo.track_recall_recency GUC (default TRUE). '
    'Returns full lesson_text + JSONB expansion for caller-chosen IDs. '
    'v0.9.1 fixes: '
    '(B1) relation_type filter replaces broken edge_kind filter — production edges had '
    'edge_kind=semantic for ALL types due to unmapped relation_types in backfill; '
    '(B2) valid_until handles both NULL and infinity sentinel conventions; '
    '(B3) bidirectional BFS — agent can discover backward relations (e.g. find cause '
    'from an effect by traversing CAUSED_BY edges in reverse); '
    '(B4) relation_types TEXT[] param — NULL traverses all, or pass specific types. '
    'Threshold default lowered: 0.5 (was 0.7) — navigation should be permissive; '
    'agent decides which connections to follow. '
    'Combine with navigate_locate() for the locate→connections→expand loop.';

-- ─────────────────────────────────────────────────────────────────────────────
-- S4: reembed() — Single-row embedding refresh
-- ─────────────────────────────────────────────────────────────────────────────
--
-- UPDATE-only: bitemporal trigger (_close_prior_version) fires on INSERT only.
-- lesson_tsv trigger fires on UPDATE OF lesson_text only — not triggered here.
-- _set_updated_at trigger fires on UPDATE — updates updated_at correctly.
-- Does NOT create a new row, does NOT change lesson_text/content_hash/id.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.reembed(
    p_lesson_id  BIGINT,
    p_new_vector vector(1024)
) RETURNS void
LANGUAGE plpgsql
AS $$
BEGIN
    IF p_new_vector IS NULL THEN
        RAISE EXCEPTION 'pgmnemo.reembed: p_new_vector must not be NULL';
    END IF;

    IF vector_dims(p_new_vector) <> 1024 THEN
        RAISE EXCEPTION 'pgmnemo.reembed: expected 1024 dims, got %',
            vector_dims(p_new_vector);
    END IF;

    UPDATE pgmnemo.agent_lesson
    SET embedding    = p_new_vector,
        embedding_at = now()
    WHERE id         = p_lesson_id
      AND is_active
      AND t_valid_to = 'infinity'::TIMESTAMPTZ;

    IF NOT FOUND THEN
        RAISE EXCEPTION 'pgmnemo.reembed: lesson % not found or not active', p_lesson_id;
    END IF;
END;
$$;

COMMENT ON FUNCTION pgmnemo.reembed(BIGINT, vector) IS
    'Refresh the embedding for a single active lesson (v0.8.0). '
    'Updates embedding + embedding_at without creating a new bitemporal row. '
    'Safe to call concurrently with ingest(): UPDATE does not fire the INSERT-only '
    '_close_prior_version trigger. Raises if lesson not found or not active. '
    'For batch refresh see reembed_batch().';

-- ─────────────────────────────────────────────────────────────────────────────
-- S5: reembed_batch() — Batch embedding refresh
-- ─────────────────────────────────────────────────────────────────────────────
--
-- Processes IDs in the order given. Caller SHOULD pass IDs in ascending order
-- to prevent deadlocks with other batch jobs.
-- FOR UPDATE SKIP LOCKED: skips rows locked by concurrent ingest()/reinforce().
-- Returns count of successfully updated rows (< input length if rows were skipped).
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.reembed_batch(
    p_lesson_ids  BIGINT[],
    p_new_vectors vector[]
) RETURNS INT
LANGUAGE plpgsql
AS $$
DECLARE
    _count  INT := 0;
    _i      INT;
    _locked BIGINT;
BEGIN
    IF p_lesson_ids IS NULL OR array_length(p_lesson_ids, 1) IS NULL THEN
        RETURN 0;
    END IF;

    IF array_length(p_lesson_ids, 1) <> array_length(p_new_vectors, 1) THEN
        RAISE EXCEPTION
            'pgmnemo.reembed_batch: ids length (%) differs from vectors length (%)',
            array_length(p_lesson_ids, 1),
            array_length(p_new_vectors, 1);
    END IF;

    FOR _i IN 1..array_length(p_lesson_ids, 1) LOOP
        -- Acquire row lock; skip rows held by concurrent writers
        SELECT id INTO _locked
        FROM pgmnemo.agent_lesson
        WHERE id         = p_lesson_ids[_i]
          AND is_active
          AND t_valid_to = 'infinity'::TIMESTAMPTZ
        FOR UPDATE SKIP LOCKED;

        IF FOUND THEN
            UPDATE pgmnemo.agent_lesson
            SET embedding    = p_new_vectors[_i],
                embedding_at = now()
            WHERE id = p_lesson_ids[_i];
            _count := _count + 1;
        END IF;
    END LOOP;

    RETURN _count;
END;
$$;

COMMENT ON FUNCTION pgmnemo.reembed_batch(BIGINT[], vector[]) IS
    'Batch embedding refresh for multiple lessons (v0.8.0). '
    'ids and vectors arrays must have the same length. '
    'Uses FOR UPDATE SKIP LOCKED: skips rows held by concurrent ingest()/reinforce(). '
    'Returns count of rows actually updated (may be < input length if rows were skipped). '
    'Lock ordering: pass IDs in ascending order to prevent deadlocks across concurrent batches. '
    'Each row processed independently — partial success is normal and expected under load.';

-- ─────────────────────────────────────────────────────────────────────────────
-- S6: recompute_content() — In-place lesson_text update
-- ─────────────────────────────────────────────────────────────────────────────
--
-- Automatic cascade on UPDATE OF lesson_text:
--   content_hash: GENERATED ALWAYS AS (MD5(...||lesson_text)) — PG recomputes automatically.
--   lesson_tsv:   pgmnemo_agent_lesson_tsv_trg fires on UPDATE OF lesson_text → refreshed.
--   updated_at:   _set_updated_at trigger fires on any UPDATE → refreshed.
-- Does NOT fire _close_prior_version (INSERT-only trigger).
-- Preserves: id, embedding, edges, provenance, confidence, source_type.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.recompute_content(
    p_lesson_id BIGINT,
    p_new_text  TEXT
) RETURNS void
LANGUAGE plpgsql
AS $$
BEGIN
    IF p_new_text IS NULL OR length(trim(p_new_text)) = 0 THEN
        RAISE EXCEPTION 'pgmnemo.recompute_content: p_new_text must be non-empty';
    END IF;

    UPDATE pgmnemo.agent_lesson
    SET lesson_text = p_new_text
    WHERE id        = p_lesson_id
      AND is_active
      AND t_valid_to = 'infinity'::TIMESTAMPTZ;

    IF NOT FOUND THEN
        RAISE EXCEPTION
            'pgmnemo.recompute_content: lesson % not found or not active',
            p_lesson_id;
    END IF;
    -- content_hash recomputed automatically (GENERATED ALWAYS AS).
    -- lesson_tsv refreshed automatically (UPDATE OF lesson_text trigger).
    -- updated_at refreshed automatically (_set_updated_at trigger).
    -- No new row created (_close_prior_version is INSERT-only).
END;
$$;

COMMENT ON FUNCTION pgmnemo.recompute_content(BIGINT, TEXT) IS
    'In-place lesson_text update without bitemporal close+create churn (v0.8.0). '
    'Cascade: content_hash recomputed (GENERATED ALWAYS AS), lesson_tsv refreshed '
    '(pgmnemo_agent_lesson_tsv_trg), updated_at refreshed (_set_updated_at). '
    'Preserves: id, embedding, mem_edges, provenance, confidence, source_type. '
    'Raises if lesson not found or not active (t_valid_to = infinity). '
    'Note: embedding remains stale after this call; follow up with reembed() if needed.';

-- ============================================================
-- pgmnemo 0.8.2 upgrade: F1 + F2 (appended from 0.8.1--0.8.2)
-- ============================================================
-- pgmnemo upgrade script: 0.8.1 → 0.8.2
-- SPDX-License-Identifier: Apache-2.0
--
-- Fixes:
--   F1 — traverse_temporal_window: unify include_unverified parsing to
--         COALESCE(current_setting(...)::BOOLEAN, FALSE), matching all other
--         recall functions (accepts on/true/1/yes, not just 'on').
--   F2 — recall_lessons + recall_hybrid: RAISE NOTICE when 0 rows returned
--         but ghost lessons (verified_at IS NULL) exist in scope, guiding
--         adopters to SET pgmnemo.include_unverified = 'on'.
--
-- All changes are body-only; no schema changes; no scoring/ranking change.

-- =============================================================================
-- F1: traverse_temporal_window — fix include_unverified parsing
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.traverse_temporal_window(
    start_id            BIGINT,
    window_interval     INTERVAL    DEFAULT INTERVAL '15 minutes',
    include_unlinked    BOOLEAN     DEFAULT TRUE,
    role_filter         TEXT        DEFAULT NULL,
    project_id_filter   INT         DEFAULT NULL,
    k                   INT         DEFAULT 20
)
RETURNS TABLE (
    lesson_id       BIGINT,
    time_delta_sec  DOUBLE PRECISION,
    linked          BOOLEAN,
    edge_weight     REAL,
    role            TEXT,
    topic           TEXT,
    lesson_text     TEXT,
    importance      SMALLINT,
    created_at      TIMESTAMPTZ,
    commit_sha      TEXT,
    verified_at     TIMESTAMPTZ
)
LANGUAGE plpgsql
STABLE
PARALLEL SAFE
AS $$
DECLARE
    _start_ts           TIMESTAMPTZ;
    _include_unverified BOOLEAN;
BEGIN
    SELECT al.created_at INTO _start_ts
    FROM pgmnemo.agent_lesson al
    WHERE al.id = start_id;

    IF NOT FOUND THEN
        RETURN;
    END IF;

    -- F1 fix: use ::BOOLEAN cast (accepts on/true/1/yes) not string-compare = 'on'
    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN,
            FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    RETURN QUERY
    WITH candidates AS (
        SELECT
            al.id,
            al.role,
            al.topic,
            al.lesson_text,
            al.importance,
            al.created_at,
            al.commit_sha,
            al.verified_at,
            ABS(EXTRACT(EPOCH FROM (al.created_at - _start_ts))) AS delta_sec
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.id <> start_id
          AND al.created_at BETWEEN (_start_ts - window_interval)
                                AND (_start_ts + window_interval)
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter         IS NULL OR al.role       = role_filter)
          AND (project_id_filter   IS NULL OR al.project_id = project_id_filter)
    ),
    edges AS (
        SELECT e.target_id AS other_id, e.weight
        FROM pgmnemo.mem_edge e
        WHERE e.source_id = start_id
          AND e.valid_until IS NULL
        UNION ALL
        SELECT e.source_id AS other_id, e.weight
        FROM pgmnemo.mem_edge e
        WHERE e.target_id = start_id
          AND e.valid_until IS NULL
    )
    SELECT
        c.id                            AS lesson_id,
        c.delta_sec                     AS time_delta_sec,
        (e.weight IS NOT NULL)          AS linked,
        e.weight                        AS edge_weight,
        c.role,
        c.topic,
        c.lesson_text,
        c.importance,
        c.created_at,
        c.commit_sha,
        c.verified_at
    FROM candidates c
    LEFT JOIN edges e ON e.other_id = c.id
    WHERE include_unlinked OR e.weight IS NOT NULL
    ORDER BY c.delta_sec ASC, c.importance DESC
    LIMIT k;
END;
$$;

COMMENT ON FUNCTION pgmnemo.traverse_temporal_window(BIGINT, INTERVAL, BOOLEAN, TEXT, INT, INT) IS
    'Return up to k agent_lesson rows whose created_at falls within ±window_interval of start_id. '
    'linked=TRUE when a mem_edge (any direction) exists between that row and start_id. '
    'include_unlinked=FALSE restricts output to explicitly connected lessons. '
    'v0.8.2 F1: include_unverified parsed via ::BOOLEAN (accepts on/true/1/yes).';


-- =============================================================================
-- F2: recall_hybrid — ghost guidance NOTICE when 0 rows returned
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;   -- F2: ghost guidance
    _fetch_k_vec        INT;   -- #4: bounded HNSW fetch
    _fetch_k_bm25       INT;   -- #4: bounded BM25 fetch
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    -- #4 C2 fix: fetch_k floors — HNSW arm respects ef_search, BM25 arm floors at 40
    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    RETURN QUERY
    WITH RECURSIVE
    -- Phase 1: HNSW vector retrieval (index scan)
    vec_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS raw_vec_score,
            0.0::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_vec
          AND al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY al.embedding <=> query_embedding   -- HNSW index scan
        LIMIT _fetch_k_vec                           -- C2: GREATEST(k*4, _ef_search)
    ),
    -- Phase 2: GIN BM25 retrieval (index scan)
    bm25_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            ts_rank_cd(
                setweight(to_tsvector('english', COALESCE(al.topic, '')), 'A') || al.lesson_tsv,
                _tsquery, 32
            )::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_text
          AND al.is_active
          AND (al.lesson_tsv @@ _tsquery
               OR to_tsvector('english', COALESCE(al.topic, '')) @@ _tsquery)
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY raw_bm25_score DESC
        LIMIT _fetch_k_bm25                          -- C2: GREATEST(k*4, 40)
    ),
    -- Merge: LEFT JOIN + anti-join UNION ALL (each id exactly once)
    all_candidates AS (
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(b.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM vec_candidates v
        LEFT JOIN bm25_candidates b ON b.id = v.id

        UNION ALL

        SELECT
            b.id, b.role, b.project_id, b.topic, b.lesson_text,
            b.importance, b.metadata, b.commit_sha, b.artifact_hash,
            b.verified_at, b.created_at, b.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            b.raw_bm25_score
        FROM bm25_candidates b
        WHERE b.id NOT IN (SELECT id FROM vec_candidates)
    ),
    -- RRF ranking over bounded candidate set
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST)   AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    )
    SELECT
        f.id                   AS lesson_id,
        f.final_score          AS score,
        f.v_score              AS vec_score,
        f.b_score              AS bm25_score,
        f.rrf_sparse           AS rrf_score,
        f.role,
        f.project_id,
        f.topic,
        f.lesson_text,
        f.importance,
        f.metadata,
        f.commit_sha,
        f.artifact_hash,
        f.verified_at,
        f.created_at,
        f.confidence::REAL,
        LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
    FROM final f
    ORDER BY f.final_score DESC, f.id ASC   -- C7: tie-breaker for determinism
    LIMIT k;

    -- F2: ghost guidance — if 0 rows returned, check for unverified (ghost) lessons in scope
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT) IS
    'Hybrid recall v0.9.0 — two bounded CTEs: HNSW vec + GIN BM25 (O(k log n) not O(n)). '
    'vec arm: LIMIT GREATEST(k*4, ef_search). bm25 arm: LIMIT GREATEST(k*4, 40). '
    'RRF (Reciprocal Rank Fusion, Cormack 2009): combines vector + BM25 ranks. '
    'Scoring: rrf_sparse + _aux_scale*(0.025*imp/5 + 0.025*conf + 0.05*recency + 0.05*prov) + delta*graph. '
    'confidence: per-lesson outcome-track-record [0,1] from reinforce(). '
    'match_confidence: vec_score (cosine similarity, [0,1]). On text-only path (NULL embedding) = 0.0. '
    'graph_proximity contributes only when mem_edge is populated; with no edges the graph term is 0. '
    'D-footgun: RAISE NOTICE when query_embedding IS NULL. '
    '#4 inclusion gated on host BENCHMARK; may revert to 0.9.1 by founder decision. '
    '17 output columns (15 existing + confidence REAL, match_confidence REAL).';


-- =============================================================================
-- F2: recall_lessons (v0.7.0, 6-arg) — ghost guidance NOTICE on vector-only path
-- Note: hybrid path delegates to recall_hybrid which issues its own notice;
--       ghost check here covers the vector-only path only (avoids double notice).
-- =============================================================================

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(
    vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ
);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT         DEFAULT 10,
    role_filter       TEXT        DEFAULT NULL,
    project_id_filter INT         DEFAULT NULL,
    query_text        TEXT        DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
PARALLEL UNSAFE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
    _ghost_count        INT;   -- F2: ghost guidance
    _track_recency      BOOLEAN;           -- v0.9.5: recall-recency stamping
BEGIN
    -- v0.9.5: read track_recall_recency GUC (default TRUE)
    BEGIN
        _track_recency := COALESCE(
            NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
            TRUE);
    EXCEPTION WHEN OTHERS THEN _track_recency := TRUE;
    END;

    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT, 2000);
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars. Original: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    _has_vec  := query_embedding IS NOT NULL;
    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _disable_hybrid := FALSE;
    END;

    IF NOT _disable_hybrid AND _has_vec AND _has_text THEN
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        -- Hybrid path: delegate to recall_hybrid (which issues its own ghost notice on 0 rows)
        RETURN QUERY
        SELECT
            h.lesson_id, h.score, h.role, h.project_id, h.topic, h.lesson_text,
            h.importance, h.metadata, h.commit_sha, h.artifact_hash,
            h.verified_at, h.created_at,
            h.vec_score, h.bm25_score, h.rrf_score,
            h.confidence, h.match_confidence
        FROM pgmnemo.recall_hybrid(
            query_embedding, _query_text, k,
            role_filter, project_id_filter, 0.4, 0.4, 60
        ) h;
        RETURN;
    END IF;

    -- Vector-only path
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05);
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION, 1.0)));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', _query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', _query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id          AS cand_id,
            al.role        AS cand_role,
            al.project_id  AS cand_project_id,
            al.topic       AS cand_topic,
            al.lesson_text AS cand_lesson_text,
            al.importance  AS cand_importance,
            al.metadata    AS cand_metadata,
            al.commit_sha  AS cand_commit_sha,
            al.artifact_hash AS cand_artifact_hash,
            al.verified_at AS cand_verified_at,
            al.created_at  AS cand_created_at,
            al.confidence  AS cand_confidence,
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score_raw,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score_raw
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
          AND (as_of_ts IS NULL
               OR (al.t_valid_from <= as_of_ts AND al.t_valid_to > as_of_ts))
          AND (al.embedding IS NOT NULL OR _has_text)
        ORDER BY al.embedding <=> query_embedding
        LIMIT GREATEST(k * 5, 50)
    ),
    anchors AS (
        SELECT cand_id FROM candidates ORDER BY vec_score_raw DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT cand_id, 0, cand_id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT reached_id AS gp_lesson_id,
               MAX(1.0 - depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk WHERE depth > 0 GROUP BY reached_id
    )
    SELECT
        c.cand_id               AS lesson_id,
        (
            0.5  * c.vec_score_raw
          + 0.15 * (c.cand_importance::DOUBLE PRECISION / 5.0)
          + 0.15 * c.cand_confidence::DOUBLE PRECISION
          + _gamma * GREATEST(0.0, 1.0 - LEAST(
                EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0))
          + 0.1 * (CASE
                WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
                ELSE 0.0 END)
          + _graph_weight * COALESCE(gp.proximity, 0.0)
        )                       AS score,
        c.cand_role             AS role,
        c.cand_project_id       AS project_id,
        c.cand_topic            AS topic,
        c.cand_lesson_text      AS lesson_text,
        c.cand_importance       AS importance,
        c.cand_metadata         AS metadata,
        c.cand_commit_sha       AS commit_sha,
        c.cand_artifact_hash    AS artifact_hash,
        c.cand_verified_at      AS verified_at,
        c.cand_created_at       AS created_at,
        c.vec_score_raw         AS vec_score,
        NULL::DOUBLE PRECISION  AS bm25_score,
        NULL::DOUBLE PRECISION  AS rrf_score,
        c.cand_confidence::REAL AS confidence,
        LEAST(1.0, GREATEST(0.0,
            (
                0.5  * c.vec_score_raw
              + 0.15 * (c.cand_importance::DOUBLE PRECISION / 5.0)
              + 0.15 * c.cand_confidence::DOUBLE PRECISION
              + _gamma * GREATEST(0.0, 1.0 - LEAST(
                    EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0))
              + 0.1 * (CASE
                    WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                    WHEN c.cand_commit_sha IS NOT NULL THEN 0.5 ELSE 0.0 END)
              + _graph_weight * COALESCE(gp.proximity, 0.0)
            ) / 1.5
        ))::REAL                AS match_confidence
    FROM candidates c
    LEFT JOIN graph_proximity gp ON gp.gp_lesson_id = c.cand_id
    ORDER BY score DESC, c.cand_importance DESC, c.cand_created_at DESC
    LIMIT k;

    -- F2: ghost guidance — vector-only path: if 0 rows, warn about excluded ghost lessons
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ) IS
    'v0.8.2 — F2: NOTICE when 0 rows returned (vector-only path) and ghost lessons exist in scope. '
    'v0.7.0 -- confidence integration + footgun guard + match_confidence. '
    'Scoring (vector path): 0.5*vec + 0.15*imp/5 + 0.15*confidence + gamma*recency + 0.1*prov + delta*graph. '
    'confidence: outcome-track-record [0,1] from reinforce(). '
    'match_confidence: LEAST(1.0, GREATEST(0.0, score/1.5)) -- interpretable [0,1] quality indicator. '
    'D-footgun: RAISE NOTICE when query_embedding IS NULL and text-only fallback active. '
    '17 output columns (15 existing + confidence REAL, match_confidence REAL). '
    'Named-column callers unaffected; positional callers: re-audit for 2 new trailing cols.';


-- =============================================================================
-- I1 OVERRIDE: recall_hybrid — restores confidence_boost_weight GUC support.
-- The v0.7.1 (migration chunk above) and v0.8.2-F2 redefinitions overwrote the
-- I1 body without _conf_boost_w. This final CREATE OR REPLACE is the canonical
-- installed version for CREATE EXTENSION pgmnemo VERSION '0.9.3'.
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;   -- F2: ghost guidance
    _fetch_k_vec        INT;   -- #4: bounded HNSW fetch
    _fetch_k_bm25       INT;   -- #4: bounded BM25 fetch
    _conf_boost_w       DOUBLE PRECISION;  -- I1: confidence boost weight
    _track_recency      BOOLEAN;           -- v0.9.5: recall-recency stamping
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    -- I1: read confidence boost weight GUC (default 0.0 = OFF, clamped [0.0, 0.01])
    BEGIN
        _conf_boost_w := GREATEST(0.0, LEAST(0.01, COALESCE(
            NULLIF(current_setting('pgmnemo.confidence_boost_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));
    EXCEPTION WHEN OTHERS THEN _conf_boost_w := 0.0;
    END;

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    -- #4 C2 fix: fetch_k floors — HNSW arm respects ef_search, BM25 arm floors at 40
    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    RETURN QUERY
    WITH RECURSIVE
    -- Phase 1: HNSW vector retrieval (index scan)
    vec_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS raw_vec_score,
            0.0::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_vec
          AND al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY al.embedding <=> query_embedding   -- HNSW index scan
        LIMIT _fetch_k_vec                           -- C2: GREATEST(k*4, _ef_search)
    ),
    -- Phase 2: GIN BM25 retrieval (index scan)
    bm25_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            ts_rank_cd(
                setweight(to_tsvector('english', COALESCE(al.topic, '')), 'A') || al.lesson_tsv,
                _tsquery, 32
            )::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_text
          AND al.is_active
          AND (al.lesson_tsv @@ _tsquery
               OR to_tsvector('english', COALESCE(al.topic, '')) @@ _tsquery)
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY raw_bm25_score DESC
        LIMIT _fetch_k_bm25                          -- C2: GREATEST(k*4, 40)
    ),
    -- Merge: LEFT JOIN + anti-join UNION ALL (each id exactly once)
    all_candidates AS (
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(b.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM vec_candidates v
        LEFT JOIN bm25_candidates b ON b.id = v.id

        UNION ALL

        SELECT
            b.id, b.role, b.project_id, b.topic, b.lesson_text,
            b.importance, b.metadata, b.commit_sha, b.artifact_hash,
            b.verified_at, b.created_at, b.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            b.raw_bm25_score
        FROM bm25_candidates b
        WHERE b.id NOT IN (SELECT id FROM vec_candidates)
    ),
    -- RRF ranking over bounded candidate set
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC) AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
              -- I1: additive zero-centered confidence boost (w=0 when GUC off → no effect)
              + _conf_boost_w * (s.confidence::DOUBLE PRECISION - 0.5)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    )
    SELECT
        f.id                   AS lesson_id,
        f.final_score          AS score,
        f.v_score              AS vec_score,
        f.b_score              AS bm25_score,
        f.rrf_sparse           AS rrf_score,
        f.role,
        f.project_id,
        f.topic,
        f.lesson_text,
        f.importance,
        f.metadata,
        f.commit_sha,
        f.artifact_hash,
        f.verified_at,
        f.created_at,
        f.confidence::REAL,
        LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
    FROM final f
    ORDER BY f.final_score DESC, f.id ASC   -- C7: tie-breaker for determinism
    LIMIT k;

    -- F2: ghost guidance — if 0 rows returned, check for unverified (ghost) lessons in scope
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT) IS
    'v0.9.2 — I1: confidence-weighted ranking (additive, zero-centered). '
    'GUC pgmnemo.confidence_boost_weight (default 0.0 = OFF, range [0.0, 0.01]). '
    'When ON: final_score += w * (confidence - 0.5). Recommended w=0.003. '
    'Cold-start (confidence=0.5) gets zero boost. High-vs-low delta at w=0.003 ≈ 0.0024 ≈ 8-15 RRF positions. '
    'graph_proximity contributes only when mem_edge is populated; with no edges the graph term is 0. '
    'v0.8.2 — F2: NOTICE when 0 rows returned and ghost lessons exist in scope. '
    'RRF (Reciprocal Rank Fusion, Cormack 2009): combines vector + BM25 ranks. '
    'Two-phase indexed retrieval: HNSW (pgvector) + GIN (BM25) → RRF fusion → graph proximity boost. '
    'match_confidence: vec_score (cosine similarity, [0,1]). On text-only path (NULL embedding) = 0.0. '
    'STABLE PARALLEL SAFE.';


-- =============================================================================
-- v0.9.6 FINAL OVERRIDES: recall_hybrid, recall_lessons, mark_stale
-- These must appear LAST in the flat install to supersede older versions
-- defined earlier in this file. All three are VOLATILE (recency stamping).
-- =============================================================================

-- =============================================================================
-- B.1) recall_hybrid() v0.9.6 — adds exclude_dag_id TEXT DEFAULT NULL (R13)
-- Must DROP older 8-arg overload because we're adding a parameter.
-- =============================================================================

DROP FUNCTION IF EXISTS pgmnemo.recall_hybrid(
    vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT
);

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60,
    exclude_dag_id    TEXT             DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;
    _fetch_k_vec        INT;
    _fetch_k_bm25       INT;
    _conf_boost_w       DOUBLE PRECISION;
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    -- I1: read confidence boost weight GUC (default 0.0 = OFF, clamped [0.0, 0.01])
    BEGIN
        _conf_boost_w := GREATEST(0.0, LEAST(0.01, COALESCE(
            NULLIF(current_setting('pgmnemo.confidence_boost_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));
    EXCEPTION WHEN OTHERS THEN _conf_boost_w := 0.0;
    END;

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    -- #4 C2 fix: fetch_k floors — HNSW arm respects ef_search, BM25 arm floors at 40
    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    RETURN QUERY
    WITH RECURSIVE
    -- Phase 1: HNSW vector retrieval (index scan)
    vec_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS raw_vec_score,
            0.0::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_vec
          AND al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          -- R13: DAG-scoped exclusion (IS DISTINCT FROM handles NULL source_dag_id gracefully)
          AND (recall_hybrid.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY al.embedding <=> query_embedding
        LIMIT _fetch_k_vec
    ),
    -- Phase 2: GIN BM25 retrieval (index scan)
    bm25_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            ts_rank_cd(
                setweight(to_tsvector('english', COALESCE(al.topic, '')), 'A') || al.lesson_tsv,
                _tsquery, 32
            )::DOUBLE PRECISION AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_text
          AND al.is_active
          AND (al.lesson_tsv @@ _tsquery
               OR to_tsvector('english', COALESCE(al.topic, '')) @@ _tsquery)
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          -- R13: DAG-scoped exclusion
          AND (recall_hybrid.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY raw_bm25_score DESC
        LIMIT _fetch_k_bm25
    ),
    -- Merge: LEFT JOIN + anti-join UNION ALL (each id exactly once)
    all_candidates AS (
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(b.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM vec_candidates v
        LEFT JOIN bm25_candidates b ON b.id = v.id

        UNION ALL

        SELECT
            b.id, b.role, b.project_id, b.topic, b.lesson_text,
            b.importance, b.metadata, b.commit_sha, b.artifact_hash,
            b.verified_at, b.created_at, b.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            b.raw_bm25_score
        FROM bm25_candidates b
        WHERE b.id NOT IN (SELECT id FROM vec_candidates)
    ),
    -- RRF ranking over bounded candidate set
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC) AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
              -- I1: additive zero-centered confidence boost (w=0 when GUC off → no effect)
              + _conf_boost_w * (s.confidence::DOUBLE PRECISION - 0.5)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    ),
    -- v0.9.5: materialise top-k results before stamping
    final_results AS MATERIALIZED (
        SELECT
            f.id                   AS lesson_id,
            f.final_score          AS score,
            f.v_score              AS vec_score,
            f.b_score              AS bm25_score,
            f.rrf_sparse           AS rrf_score,
            f.role,
            f.project_id,
            f.topic,
            f.lesson_text,
            f.importance,
            f.metadata,
            f.commit_sha,
            f.artifact_hash,
            f.verified_at,
            f.created_at,
            f.confidence::REAL,
            LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
        FROM final f
        ORDER BY f.final_score DESC, f.id ASC
        LIMIT k
    ),
    -- v0.9.5: stamp recency on returned lessons (runs always, gated by GUC)
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT lesson_id FROM final_results))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT
        fr.lesson_id,
        fr.score,
        fr.vec_score,
        fr.bm25_score,
        fr.rrf_score,
        fr.role,
        fr.project_id,
        fr.topic,
        fr.lesson_text,
        fr.importance,
        fr.metadata,
        fr.commit_sha,
        fr.artifact_hash,
        fr.verified_at,
        fr.created_at,
        fr.confidence,
        fr.match_confidence
    FROM final_results fr
    ORDER BY fr.score DESC, fr.lesson_id ASC;

    -- F2: ghost guidance — if 0 rows returned, check for unverified (ghost) lessons in scope
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT, TEXT) IS
    'v0.9.6 — R13: exclude_dag_id TEXT DEFAULT NULL parameter. When set, suppresses lessons '
    'whose source_dag_id matches the given value (IS DISTINCT FROM semantics: NULL source_dag_id rows '
    'always pass). Allows a workflow to exclude its own output from recall during the same run. '
    'v0.9.5 — Recall-recency stamping: stamps last_recalled_at + recall_count on returned lessons '
    'via data-modifying CTE (runs atomically with the SELECT, no double-scan). '
    'GUC pgmnemo.track_recall_recency (bool, default ON): set to off to disable stamping. '
    'v0.9.2 — I1: confidence-weighted ranking (additive, zero-centered). '
    'GUC pgmnemo.confidence_boost_weight (default 0.0 = OFF, range [0.0, 0.01]). '
    'When ON: final_score += w * (confidence - 0.5). Recommended w=0.003. '
    'Cold-start (confidence=0.5) gets zero boost. High-vs-low delta at w=0.003 ≈ 0.0024 ≈ 8-15 RRF positions. '
    'graph_proximity contributes only when mem_edge is populated; with no edges the graph term is 0. '
    'v0.8.2 — F2: NOTICE when 0 rows returned and ghost lessons exist in scope. '
    'RRF (Reciprocal Rank Fusion, Cormack 2009): combines vector + BM25 ranks. '
    'Two-phase indexed retrieval: HNSW (pgvector) + GIN (BM25) → RRF fusion → graph proximity boost. '
    'match_confidence: vec_score (cosine similarity, [0,1]). On text-only path (NULL embedding) = 0.0. '
    '17 output columns. VOLATILE (side-effects: recency stamp).';

-- =============================================================================
-- B.2) recall_lessons() v0.9.6 — adds exclude_dag_id TEXT DEFAULT NULL (R13)
--      Must DROP the old 6-arg overload because we're adding a parameter.
-- =============================================================================

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(
    vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ
);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT         DEFAULT 10,
    role_filter       TEXT        DEFAULT NULL,
    project_id_filter INT         DEFAULT NULL,
    query_text        TEXT        DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ DEFAULT NULL,
    exclude_dag_id    TEXT        DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
    _ghost_count        INT;
BEGIN
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT, 2000);
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars. Original: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    _has_vec  := query_embedding IS NOT NULL;
    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _disable_hybrid := FALSE;
    END;

    -- Hybrid path delegates to recall_hybrid (which handles stamping + exclude_dag_id)
    IF NOT _disable_hybrid AND _has_vec AND _has_text THEN
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        RETURN QUERY
        SELECT
            h.lesson_id, h.score, h.role, h.project_id, h.topic, h.lesson_text,
            h.importance, h.metadata, h.commit_sha, h.artifact_hash,
            h.verified_at, h.created_at,
            h.vec_score, h.bm25_score, h.rrf_score,
            h.confidence, h.match_confidence
        FROM pgmnemo.recall_hybrid(
            query_embedding, _query_text, k,
            role_filter, project_id_filter, 0.4, 0.4, 60,
            exclude_dag_id
        ) h;
        RETURN;
    END IF;

    -- Vector-only path
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05);
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION, 1.0)));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', _query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', _query_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id          AS cand_id,
            al.role        AS cand_role,
            al.project_id  AS cand_project_id,
            al.topic       AS cand_topic,
            al.lesson_text AS cand_lesson_text,
            al.importance  AS cand_importance,
            al.metadata    AS cand_metadata,
            al.commit_sha  AS cand_commit_sha,
            al.artifact_hash AS cand_artifact_hash,
            al.verified_at AS cand_verified_at,
            al.created_at  AS cand_created_at,
            al.confidence  AS cand_confidence,
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score_raw,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score_raw
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
          -- R13: DAG-scoped exclusion (vector-only path)
          AND (exclude_dag_id IS NULL OR al.source_dag_id IS DISTINCT FROM exclude_dag_id)
          AND (as_of_ts IS NULL
               OR (al.t_valid_from <= as_of_ts AND al.t_valid_to > as_of_ts))
          AND (al.embedding IS NOT NULL OR _has_text)
        ORDER BY al.embedding <=> query_embedding
        LIMIT GREATEST(k * 5, 50)
    ),
    anchors AS (
        SELECT cand_id FROM candidates ORDER BY vec_score_raw DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT cand_id, 0, cand_id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT reached_id AS gp_lesson_id,
               MAX(1.0 - depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk WHERE depth > 0 GROUP BY reached_id
    ),
    -- v0.9.5: materialise top-k before stamping
    result_rows AS MATERIALIZED (
        SELECT
            c.cand_id               AS lesson_id,
            (
                0.5  * c.vec_score_raw
              + 0.15 * (c.cand_importance::DOUBLE PRECISION / 5.0)
              + 0.15 * c.cand_confidence::DOUBLE PRECISION
              + _gamma * GREATEST(0.0, 1.0 - LEAST(
                    EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0))
              + 0.1 * (CASE
                    WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                    WHEN c.cand_commit_sha IS NOT NULL THEN 0.5
                    ELSE 0.0 END)
              + _graph_weight * COALESCE(gp.proximity, 0.0)
            )                       AS score,
            c.cand_role             AS role,
            c.cand_project_id       AS project_id,
            c.cand_topic            AS topic,
            c.cand_lesson_text      AS lesson_text,
            c.cand_importance       AS importance,
            c.cand_metadata         AS metadata,
            c.cand_commit_sha       AS commit_sha,
            c.cand_artifact_hash    AS artifact_hash,
            c.cand_verified_at      AS verified_at,
            c.cand_created_at       AS created_at,
            c.vec_score_raw         AS vec_score,
            NULL::DOUBLE PRECISION  AS bm25_score,
            NULL::DOUBLE PRECISION  AS rrf_score,
            c.cand_confidence::REAL AS confidence,
            LEAST(1.0, GREATEST(0.0,
                (
                    0.5  * c.vec_score_raw
                  + 0.15 * (c.cand_importance::DOUBLE PRECISION / 5.0)
                  + 0.15 * c.cand_confidence::DOUBLE PRECISION
                  + _gamma * GREATEST(0.0, 1.0 - LEAST(
                        EXTRACT(EPOCH FROM (NOW() - c.cand_created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.1 * (CASE
                        WHEN c.cand_commit_sha IS NOT NULL AND c.cand_verified_at IS NOT NULL THEN 1.0
                        WHEN c.cand_commit_sha IS NOT NULL THEN 0.5 ELSE 0.0 END)
                  + _graph_weight * COALESCE(gp.proximity, 0.0)
                ) / 1.5
            ))::REAL                AS match_confidence
        FROM candidates c
        LEFT JOIN graph_proximity gp ON gp.gp_lesson_id = c.cand_id
        ORDER BY score DESC, c.cand_importance DESC, c.cand_created_at DESC
        LIMIT k
    ),
    -- v0.9.5: stamp recency on returned lessons (runs always, gated by GUC)
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT rr.lesson_id FROM result_rows rr))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT
        rr.lesson_id, rr.score, rr.role, rr.project_id, rr.topic, rr.lesson_text,
        rr.importance, rr.metadata, rr.commit_sha, rr.artifact_hash,
        rr.verified_at, rr.created_at,
        rr.vec_score, rr.bm25_score, rr.rrf_score,
        rr.confidence, rr.match_confidence
    FROM result_rows rr
    ORDER BY rr.score DESC, rr.importance DESC, rr.created_at DESC;

    -- F2: ghost guidance — vector-only path: if 0 rows, warn about excluded ghost lessons
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (role_filter IS NULL OR al.role = role_filter)
          AND (project_id_filter IS NULL OR al.project_id = project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ, TEXT) IS
    'v0.9.6 — R13: exclude_dag_id TEXT DEFAULT NULL parameter. When set, suppresses lessons '
    'whose source_dag_id matches the given value (IS DISTINCT FROM semantics). '
    'On the hybrid path, exclude_dag_id is forwarded to recall_hybrid(). '
    'v0.9.5 — Recall-recency stamping on vector-only path (hybrid path delegates to recall_hybrid). '
    'GUC pgmnemo.track_recall_recency (bool, default ON): set to off to disable stamping. '
    'v0.8.2 — F2: NOTICE when 0 rows returned (vector-only path) and ghost lessons exist in scope. '
    'v0.7.0 -- confidence integration + footgun guard + match_confidence. '
    'Scoring (vector path): 0.5*vec + 0.15*imp/5 + 0.15*confidence + gamma*recency + 0.1*prov + delta*graph. '
    '17 output columns. VOLATILE (side-effects: recency stamp).';

-- =============================================================================
-- B.3) navigate_locate() v0.9.5 — VOLATILE, recency stamp via data-modifying CTE
-- Must DROP because STABLE → VOLATILE.
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.navigate_locate(
    query_embedding   vector(1024),
    query_text        TEXT,
    token_budget_chars INT              DEFAULT 2000,
    jsonb_filter      JSONB             DEFAULT NULL,
    project_id_filter INT               DEFAULT NULL
)
RETURNS TABLE (
    id              BIGINT,
    preview         TEXT,
    score           FLOAT8,
    tokens_consumed INT,
    navigation_path TEXT
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 2;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _vec_weight         CONSTANT DOUBLE PRECISION := 0.4;
    _bm25_weight        CONSTANT DOUBLE PRECISION := 0.4;
    _raw_blend_weight   DOUBLE PRECISION;
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;
    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION 'pgmnemo.navigate_locate: both query_embedding and query_text are NULL/empty';
    END IF;

    _rrf_k_f := 60.0;
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN
        _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('english', query_text);
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    raw_candidates AS (
        SELECT
            al.id,
            al.topic_tsv,
            al.lesson_tsv,
            al.lesson_text,
            al.importance,
            al.commit_sha,
            al.verified_at,
            al.created_at,
            al.metadata,
            length(al.lesson_text)                                            AS text_len,
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_vec_score,
            CASE
                WHEN _has_text AND (al.topic_tsv @@ _tsquery OR al.lesson_tsv @@ _tsquery)
                THEN ts_rank_cd(
                    setweight(al.topic_tsv, 'A') || al.lesson_tsv,
                    _tsquery, 32)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (navigate_locate.project_id_filter IS NULL
               OR al.project_id = navigate_locate.project_id_filter)
          AND (navigate_locate.jsonb_filter IS NULL
               OR al.metadata @> navigate_locate.jsonb_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
          AND (
                  (_has_vec  AND al.embedding IS NOT NULL)
               OR (_has_text AND (al.topic_tsv @@ _tsquery OR al.lesson_tsv @@ _tsquery))
          )
    ),
    rrf_ranked AS (
        SELECT *,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC)  AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK()   OVER (PARTITION BY (raw_bm25_score > 0)
                                     ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                                  AS bm25_rank_sparse,
            COUNT(*) OVER ()                                                     AS n_candidates
        FROM raw_candidates
    ),
    scored AS (
        SELECT
            r.id, r.text_len, r.lesson_text, r.metadata, r.importance,
            r.commit_sha, r.verified_at, r.created_at,
            r.vec_rank, r.n_candidates,
            CASE WHEN r.bm25_rank_sparse IS NOT NULL THEN r.bm25_rank_sparse
                 ELSE r.n_candidates + 1
            END AS bm25_rank_eff,
            (
                _vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
              + _bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                     r.n_candidates + 1)::DOUBLE PRECISION)
              + _raw_blend_weight * (
                    _vec_weight  * r.raw_vec_score
                  + _bm25_weight * r.raw_bm25_score)
            ) AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT
            gw.anchor_id,
            gw.depth + 1,
            CASE WHEN me.source_id = gw.reached_id
                    THEN me.target_id
                    ELSE me.source_id
               END
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON (
            me.source_id = gw.reached_id OR me.target_id = gw.reached_id
        )
        WHERE gw.depth < _max_depth
          AND (me.valid_until IS NULL OR me.valid_until = 'infinity'::TIMESTAMPTZ)
    ),
    graph_proximity AS (
        SELECT
            gw.reached_id AS lesson_id,
            MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw
        WHERE gw.depth > 0
        GROUP BY gw.reached_id
    ),
    final_ranked AS (
        SELECT
            s.id,
            s.text_len,
            s.vec_rank,
            s.bm25_rank_eff,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.05 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.05 * GREATEST(0.0,
                               1.0 - LEAST(
                                   EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0),
                                   1.0
                               )
                           )::DOUBLE PRECISION
                  + 0.05 * (CASE
                              WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                              WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                              ELSE                                                             0.0
                            END)::DOUBLE PRECISION
                )
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
        ORDER BY (
                s.rrf_sparse
              + _aux_scale * (
                    0.05 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.05 * GREATEST(0.0,
                               1.0 - LEAST(
                                   EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0),
                                   1.0
                               )
                           )::DOUBLE PRECISION
                  + 0.05 * (CASE
                              WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                              WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                              ELSE                                                             0.0
                            END)::DOUBLE PRECISION
                )
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
            DESC
        LIMIT 200
    ),
    budget_window AS (
        SELECT
            fr.id,
            fr.final_score,
            fr.text_len,
            fr.vec_rank,
            fr.bm25_rank_eff,
            SUM(fr.text_len) OVER (
                ORDER BY fr.final_score DESC, fr.id ASC
                ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
            ) AS cum_chars,
            ROW_NUMBER() OVER (ORDER BY fr.final_score DESC, fr.id ASC) AS rn
        FROM final_ranked fr
    ),
    -- v0.9.5: materialise budget result before stamping
    located_rows AS MATERIALIZED (
        SELECT
            bw.id,
            left(al.lesson_text, 50)  AS preview,
            bw.final_score::FLOAT8    AS score,
            bw.cum_chars::INT         AS tokens_consumed,
            CASE
                WHEN jsonb_filter IS NOT NULL THEN 'jsonb_gate'
                WHEN bw.vec_rank <= bw.bm25_rank_eff THEN 'vector'
                ELSE 'bm25'
            END                       AS navigation_path
        FROM budget_window bw
        JOIN pgmnemo.agent_lesson al ON al.id = bw.id
        WHERE bw.rn = 1
           OR (bw.cum_chars - bw.text_len) < token_budget_chars
        ORDER BY bw.final_score DESC, bw.id ASC
    ),
    -- v0.9.5: stamp recency on located lessons
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT lr.id FROM located_rows lr))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT lr.id, lr.preview, lr.score, lr.tokens_consumed, lr.navigation_path
    FROM located_rows lr
    ORDER BY lr.score DESC, lr.id ASC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.navigate_locate(vector, TEXT, INT, JSONB, INT) IS
    'v0.9.5 — Recall-recency stamping: stamps last_recalled_at + recall_count on returned lessons. '
    'GUC pgmnemo.track_recall_recency (bool, default ON). Changed STABLE → VOLATILE. '
    'v0.9.1 fixes: (1) graph_walk traverses ALL relation_types bidirectionally; '
    '(2) raw_candidates uses stored topic_tsv column (GIN-indexed). '
    'Returns id/preview/score/tokens_consumed/navigation_path. '
    'token_budget_chars: cumulative char limit; first row always returned. '
    'jsonb_filter: WHERE metadata @> jsonb_filter pushed into candidate scan. '
    'project_id_filter: scopes candidates to a single project. '
    'Combine with navigate_expand() to retrieve content for chosen IDs.';

-- =============================================================================
-- B.4) navigate_expand() v0.9.5 — VOLATILE, recency stamp via data-modifying CTE
-- Must DROP because STABLE → VOLATILE.
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.navigate_expand(
    ids                    BIGINT[],
    expand_fields          TEXT[]           DEFAULT '{}',
    graph_expand_depth     INT              DEFAULT 1,
    graph_expand_threshold FLOAT            DEFAULT 0.5,
    relation_types         TEXT[]           DEFAULT NULL
)
RETURNS TABLE (
    id                      BIGINT,
    content                 TEXT,
    expand_detail           JSONB,
    graph_neighbor_ids      BIGINT[],
    graph_neighbor_previews TEXT[],
    tokens_consumed         INT,
    navigation_path         TEXT
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
BEGIN
    IF ids IS NULL OR array_length(ids, 1) IS NULL THEN
        RETURN;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    -- Step 1: seed rows — the requested IDs
    seed_rows AS (
        SELECT
            al.id,
            al.lesson_text,
            al.metadata,
            CASE
                WHEN expand_fields IS NOT NULL AND array_length(expand_fields, 1) > 0
                THEN (
                    SELECT jsonb_object_agg(f, al.metadata->f)
                    FROM unnest(expand_fields) AS f
                    WHERE al.metadata ? f
                )
                ELSE NULL::JSONB
            END                                                            AS expand_detail,
            'content'::TEXT                                                AS navigation_path,
            0                                                              AS depth,
            ARRAY[al.id]                                                   AS path
        FROM pgmnemo.agent_lesson al
        WHERE al.id = ANY(ids)
          AND al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
    ),
    -- Step 2: BFS graph expansion — BIDIRECTIONAL, relation_type-gated, weight-gated
    graph_expand (node_id, lesson_text, metadata, depth, path) AS (
        SELECT sr.id, sr.lesson_text, sr.metadata, 0, sr.path
        FROM seed_rows sr

        UNION ALL

        SELECT
            al.id,
            al.lesson_text,
            al.metadata,
            ge.depth + 1,
            ge.path || al.id
        FROM graph_expand ge
        JOIN pgmnemo.mem_edge me ON (
            me.source_id = ge.node_id OR me.target_id = ge.node_id
        )
        JOIN pgmnemo.agent_lesson al ON al.id = CASE
            WHEN me.source_id = ge.node_id THEN me.target_id
            ELSE me.source_id
        END
        WHERE ge.depth < graph_expand_depth
          AND al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND NOT (al.id = ANY(ge.path))
          AND (relation_types IS NULL OR me.relation_type = ANY(relation_types))
          AND me.weight >= graph_expand_threshold
          AND (me.valid_until IS NULL OR me.valid_until = 'infinity'::TIMESTAMPTZ)
    ),
    -- Step 3: expanded rows — neighbors not in seed set
    expanded_rows AS (
        SELECT DISTINCT ON (ge.node_id)
            ge.node_id AS id,
            ge.lesson_text,
            ge.metadata,
            CASE
                WHEN expand_fields IS NOT NULL AND array_length(expand_fields, 1) > 0
                THEN (
                    SELECT jsonb_object_agg(f, ge.metadata->f)
                    FROM unnest(expand_fields) AS f
                    WHERE ge.metadata ? f
                )
                ELSE NULL::JSONB
            END AS expand_detail,
            'graph_expand'::TEXT AS navigation_path
        FROM graph_expand ge
        WHERE ge.depth > 0
          AND NOT (ge.node_id = ANY(ids))
        ORDER BY ge.node_id, ge.depth ASC
    ),
    -- Step 4a: distinct neighbors per seed
    distinct_neighbors AS (
        SELECT
            ge.path[1]   AS seed_id,
            ge.node_id,
            ge.depth,
            left(ge.lesson_text, 50)  AS neighbor_preview
        FROM graph_expand ge
        WHERE ge.depth > 0
          AND NOT (ge.node_id = ANY(ids))
        ORDER BY ge.path[1], ge.node_id, ge.depth ASC
    ),
    neighbor_summary AS (
        SELECT
            dn.seed_id,
            array_agg(dn.node_id ORDER BY dn.depth, dn.node_id)          AS neighbor_ids,
            array_agg(dn.neighbor_preview ORDER BY dn.depth, dn.node_id) AS neighbor_previews
        FROM distinct_neighbors dn
        GROUP BY dn.seed_id
    ),
    -- Step 5: union seed + expanded
    combined AS (
        SELECT sr.id,
               sr.lesson_text                                  AS content,
               sr.expand_detail,
               ns.neighbor_ids                                 AS graph_neighbor_ids,
               ns.neighbor_previews                            AS graph_neighbor_previews,
               sr.navigation_path
        FROM seed_rows sr
        LEFT JOIN neighbor_summary ns ON ns.seed_id = sr.id

        UNION ALL

        SELECT er.id,
               er.lesson_text                                  AS content,
               er.expand_detail,
               NULL::BIGINT[]                                  AS graph_neighbor_ids,
               NULL::TEXT[]                                    AS graph_neighbor_previews,
               er.navigation_path
        FROM expanded_rows er
    ),
    -- v0.9.5: materialise combined before stamping
    expand_results AS MATERIALIZED (
        SELECT
            c.id,
            c.content,
            c.expand_detail,
            c.graph_neighbor_ids,
            c.graph_neighbor_previews,
            SUM(length(c.content)) OVER (
                ORDER BY c.id ASC
                ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW
            )::INT  AS tokens_consumed,
            c.navigation_path
        FROM combined c
        ORDER BY c.id ASC
    ),
    -- v0.9.5: stamp recency on all returned lesson IDs
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT er.id FROM expand_results er))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT
        er.id,
        er.content,
        er.expand_detail,
        er.graph_neighbor_ids,
        er.graph_neighbor_previews,
        er.tokens_consumed,
        er.navigation_path
    FROM expand_results er
    ORDER BY er.id ASC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.navigate_expand(BIGINT[], TEXT[], INT, FLOAT, TEXT[]) IS
    'v0.9.5 — Recall-recency stamping: stamps last_recalled_at + recall_count on all returned IDs '
    '(seeds + graph-expanded neighbors). GUC pgmnemo.track_recall_recency (bool, default ON). '
    'Changed STABLE → VOLATILE. '
    'v0.9.1 fixes: '
    '(B1) relation_type filter replaces broken edge_kind filter; '
    '(B2) valid_until handles both NULL and infinity sentinel conventions; '
    '(B3) bidirectional BFS; '
    '(B4) relation_types TEXT[] param — NULL traverses all. '
    'Threshold default 0.5 (was 0.7). Combine with navigate_locate() for locate→expand loop.';

-- =============================================================================
-- C) pgmnemo.mark_stale() — usage-based corpus curation
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.mark_stale(
    p_unused_days         INT     DEFAULT 45,
    p_min_confidence_keep REAL    DEFAULT 0.6,
    p_keep_provenance     BOOLEAN DEFAULT TRUE,
    p_dry_run             BOOLEAN DEFAULT TRUE,
    p_cap                 INT     DEFAULT 500
)
RETURNS TABLE(
    lesson_id        BIGINT,
    role             TEXT,
    topic            TEXT,
    last_recalled_at TIMESTAMPTZ,
    confidence       REAL,
    would_deprecate  BOOLEAN
)
LANGUAGE plpgsql
AS $func$
#variable_conflict use_column
DECLARE
    _candidate_count INT;
    _cutoff          TIMESTAMPTZ;
BEGIN
    _cutoff := NOW() - (p_unused_days * INTERVAL '1 day');

    -- Count candidates that would_deprecate (not in any safeguard)
    SELECT COUNT(*) INTO _candidate_count
    FROM pgmnemo.agent_lesson al
    WHERE al.is_active
      AND al.state NOT IN ('deprecated', 'archived', 'rejected')
      AND (
          -- Never recalled within window
          (al.last_recalled_at IS NOT NULL AND al.last_recalled_at < _cutoff)
          OR
          -- Never recalled at all and old enough
          (al.last_recalled_at IS NULL AND al.created_at < _cutoff)
      )
      -- Not in any safeguard
      AND al.confidence < p_min_confidence_keep
      AND al.importance < 5
      AND NOT (p_keep_provenance
               AND (al.commit_sha IS NOT NULL OR al.artifact_hash IS NOT NULL));

    -- Cap guard: refuse to deprecate without explicit higher cap
    IF NOT p_dry_run AND _candidate_count > p_cap THEN
        RAISE NOTICE
            'pgmnemo.mark_stale: % candidates exceed cap %. No action taken. '
            'Re-run with p_cap=>% to proceed, or narrow criteria first.',
            _candidate_count, p_cap, _candidate_count;
        -- Still return candidate list for review
        RETURN QUERY
        SELECT
            al.id                  AS lesson_id,
            al.role,
            al.topic,
            al.last_recalled_at,
            al.confidence,
            -- would_deprecate: not in any safeguard
            (al.confidence < p_min_confidence_keep
             AND al.importance < 5
             AND NOT (p_keep_provenance
                      AND (al.commit_sha IS NOT NULL OR al.artifact_hash IS NOT NULL))
            ) AS would_deprecate
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.state NOT IN ('deprecated', 'archived', 'rejected')
          AND (
              (al.last_recalled_at IS NOT NULL AND al.last_recalled_at < _cutoff)
              OR
              (al.last_recalled_at IS NULL AND al.created_at < _cutoff)
          )
        ORDER BY al.last_recalled_at ASC NULLS FIRST, al.created_at ASC;
        RETURN;
    END IF;

    -- Dry-run or within cap: return candidates with would_deprecate flag
    RETURN QUERY
    SELECT
        al.id                  AS lesson_id,
        al.role,
        al.topic,
        al.last_recalled_at,
        al.confidence,
        (al.confidence < p_min_confidence_keep
         AND al.importance < 5
         AND NOT (p_keep_provenance
                  AND (al.commit_sha IS NOT NULL OR al.artifact_hash IS NOT NULL))
        ) AS would_deprecate
    FROM pgmnemo.agent_lesson al
    WHERE al.is_active
      AND al.state NOT IN ('deprecated', 'archived', 'rejected')
      AND (
          (al.last_recalled_at IS NOT NULL AND al.last_recalled_at < _cutoff)
          OR
          (al.last_recalled_at IS NULL AND al.created_at < _cutoff)
      )
    ORDER BY al.last_recalled_at ASC NULLS FIRST, al.created_at ASC;

    -- Actual deprecation (only when NOT dry_run AND within cap)
    IF NOT p_dry_run THEN
        -- Direct UPDATE bypasses the state-machine guard intentionally:
        -- mark_stale() is an operator-level curation primitive; eligible lessons
        -- may be in any active state (draft, candidate, validated, canonical).
        -- Unlike transition_lesson(), we do NOT enforce a specific from-state.
        UPDATE pgmnemo.agent_lesson
        SET state            = 'deprecated',
            state_changed_at = NOW()
        WHERE is_active
          AND state NOT IN ('deprecated', 'archived', 'rejected')
          AND (
              (last_recalled_at IS NOT NULL AND last_recalled_at < _cutoff)
              OR
              (last_recalled_at IS NULL AND created_at < _cutoff)
          )
          AND confidence < p_min_confidence_keep
          AND importance < 5
          AND NOT (p_keep_provenance
                   AND (commit_sha IS NOT NULL OR artifact_hash IS NOT NULL));
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.mark_stale(INT, REAL, BOOLEAN, BOOLEAN, INT) IS
    'v0.9.5 — Usage-based corpus curation. Identifies and optionally deprecates '
    'lessons unused for p_unused_days (default 45). '
    'Candidates: active lessons where last_recalled_at < cutoff OR (NULL AND created_at < cutoff). '
    'Safeguards (never touched): '
    '  confidence >= p_min_confidence_keep (default 0.6), '
    '  importance = 5, '
    '  p_keep_provenance=TRUE AND (commit_sha IS NOT NULL OR artifact_hash IS NOT NULL). '
    'p_dry_run=TRUE (default): returns candidates without modifying. SAFE to run anytime. '
    'p_dry_run=FALSE: directly sets state=''deprecated'' on each eligible candidate (bypasses state-machine guard — intentional for operator curation). '
    'p_cap (default 500): if eligible candidates > cap, raises NOTICE and takes NO action — '
    'caller must explicitly set p_cap higher. '
    'Returns all candidates with would_deprecate flag (TRUE = not in any safeguard). '
    'ALWAYS review the dry_run output before running with p_dry_run=>FALSE.';


-- ====== v0.10.0 additions ======

-- ============================================================================
-- SUPPORTING INDEX: entity content_type GIN index (BM25 dispatch path)
-- ============================================================================

CREATE INDEX IF NOT EXISTS ix_pgmnemo_lesson_tsv_entity
    ON pgmnemo.agent_lesson USING gin (lesson_tsv)
    WHERE content_type = 'entity' AND is_active = TRUE;

CREATE INDEX IF NOT EXISTS ix_pgmnemo_content_type_active
    ON pgmnemo.agent_lesson (content_type)
    WHERE is_active = TRUE AND content_type IS NOT NULL;

CREATE INDEX IF NOT EXISTS ix_pgmnemo_temporal_content_type
    ON pgmnemo.agent_lesson (t_valid_from DESC)
    WHERE content_type = 'temporal' AND is_active = TRUE;

-- ============================================================================
-- 1. navigate_locate_dispatch — per-type access-path router
-- ============================================================================
--
-- Routes each query to the cheapest adequate index per content_type:
--   'entity'   → GIN BM25 on lesson_tsv/topic_tsv  (navigation_path='bm25_entity')
--   'temporal' → btree on t_valid_from DESC          (navigation_path='temporal_btree')
--   'relation' → mem_edge BFS from BM25 seed         (navigation_path='graph_relation')
--   NULL       → delegate to existing navigate_locate (navigation_path varies: vector/bm25/hybrid)
--
-- Return schema: identical to navigate_locate (id, preview, score, tokens_consumed, navigation_path)
-- Safe: does not replace navigate_locate; coexists as separate function.
-- ============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.navigate_locate_dispatch(
    query_embedding         vector(1024),
    query_text              text,
    token_budget_chars      integer      DEFAULT 2000,
    jsonb_filter            jsonb        DEFAULT NULL,
    project_id_filter       integer      DEFAULT NULL,
    content_type_dispatch   text         DEFAULT NULL   -- NULL = unified HNSW+BM25 path
)
RETURNS TABLE (
    id               bigint,
    preview          text,
    score            double precision,
    tokens_consumed  integer,
    navigation_path  text
)
LANGUAGE plpgsql
STABLE PARALLEL SAFE
AS $$
DECLARE
    _tsquery        TSQUERY;
    _has_text       BOOLEAN;
    _running_tokens INT := 0;
BEGIN
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    -- ─────────────────────────────────────────────────────────────────────
    -- PATH A: ENTITY DISPATCH — GIN BM25, no HNSW
    -- Handles content_type='entity' rows (canonical names, technologies, orgs)
    -- BM25 is the cheapest adequate path: exact/fuzzy name match via GIN tsvector
    -- ─────────────────────────────────────────────────────────────────────
    IF content_type_dispatch = 'entity' THEN
        IF NOT _has_text THEN
            RAISE EXCEPTION 'pgmnemo.navigate_locate_dispatch: entity dispatch requires non-empty query_text';
        END IF;
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            _tsquery := plainto_tsquery('english', query_text);
        END;

        RETURN QUERY
        WITH entity_candidates AS (
            SELECT
                al.id,
                left(al.lesson_text, 120)::TEXT                                  AS preview,
                ts_rank_cd(
                    setweight(al.topic_tsv, 'A') || al.lesson_tsv,
                    _tsquery, 32
                )::DOUBLE PRECISION                                               AS score,
                length(al.lesson_text)                                           AS chars
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND al.content_type = 'entity'
              AND (al.lesson_tsv @@ _tsquery OR al.topic_tsv @@ _tsquery)
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
              AND (jsonb_filter IS NULL OR al.metadata @> jsonb_filter)
            ORDER BY score DESC
            LIMIT 50
        ),
        budget_cumsum AS (
            SELECT
                ec.id,
                ec.preview,
                ec.score,
                ec.chars,
                SUM(ec.chars) OVER (ORDER BY ec.score DESC ROWS UNBOUNDED PRECEDING) AS cumsum
            FROM entity_candidates ec
        )
        SELECT
            bc.id,
            bc.preview,
            bc.score,
            LEAST(bc.cumsum, token_budget_chars)::INT                            AS tokens_consumed,
            'bm25_entity'::TEXT                                                   AS navigation_path
        FROM budget_cumsum bc
        WHERE bc.cumsum - bc.chars < token_budget_chars;
        RETURN;
    END IF;

    -- ─────────────────────────────────────────────────────────────────────
    -- PATH B: TEMPORAL DISPATCH — btree on t_valid_from, no HNSW
    -- Handles "what happened recently / in window X" queries
    -- Recency ordering = cheapest adequate path for temporal content
    -- ─────────────────────────────────────────────────────────────────────
    IF content_type_dispatch = 'temporal' THEN
        RETURN QUERY
        WITH temporal_candidates AS (
            SELECT
                al.id,
                left(al.lesson_text, 120)::TEXT                                  AS preview,
                -- Score = recency: normalized epoch (recent = higher score)
                GREATEST(
                    1.0 - (EXTRACT(EPOCH FROM (NOW() - COALESCE(al.t_valid_from, al.created_at)))
                           / 86400.0 / 365.0),
                    0.0
                )::DOUBLE PRECISION                                               AS score,
                length(al.lesson_text)                                           AS chars
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND al.content_type = 'temporal'
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
              AND (jsonb_filter IS NULL OR al.metadata @> jsonb_filter)
            ORDER BY al.t_valid_from DESC NULLS LAST
            LIMIT 50
        ),
        budget_cumsum AS (
            SELECT
                tc.id, tc.preview, tc.score, tc.chars,
                SUM(tc.chars) OVER (ORDER BY tc.score DESC ROWS UNBOUNDED PRECEDING) AS cumsum
            FROM temporal_candidates tc
        )
        SELECT
            bc.id,
            bc.preview,
            bc.score,
            LEAST(bc.cumsum, token_budget_chars)::INT,
            'temporal_btree'::TEXT
        FROM budget_cumsum bc
        WHERE bc.cumsum - bc.chars < token_budget_chars;
        RETURN;
    END IF;

    -- ─────────────────────────────────────────────────────────────────────
    -- PATH C: RELATION DISPATCH — graph traversal from BM25 seed
    -- Handles "what caused / is related to X" queries
    -- Seeds via BM25, then follows mem_edge to connected items
    -- ─────────────────────────────────────────────────────────────────────
    IF content_type_dispatch = 'relation' THEN
        IF NOT _has_text THEN
            RAISE EXCEPTION 'pgmnemo.navigate_locate_dispatch: relation dispatch requires non-empty query_text';
        END IF;
        BEGIN
            _tsquery := websearch_to_tsquery('english', query_text);
        EXCEPTION WHEN OTHERS THEN
            _tsquery := plainto_tsquery('english', query_text);
        END;

        RETURN QUERY
        WITH seed_ids AS (
            -- BM25 seed: find anchor lessons matching the query
            SELECT al.id AS seed_id, ts_rank_cd(al.lesson_tsv, _tsquery, 32)::DOUBLE PRECISION AS seed_score
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND (al.lesson_tsv @@ _tsquery OR al.topic_tsv @@ _tsquery)
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
            ORDER BY seed_score DESC
            LIMIT 10
        ),
        graph_neighbors AS (
            -- BFS 1-hop from seeds via mem_edge
            SELECT DISTINCT
                al.id,
                left(al.lesson_text, 120)::TEXT                                  AS preview,
                (me.weight * 0.7 + s.seed_score * 0.3)::DOUBLE PRECISION        AS score,
                length(al.lesson_text)                                           AS chars
            FROM seed_ids s
            JOIN pgmnemo.mem_edge me ON (me.source_id = s.seed_id OR me.target_id = s.seed_id)
            JOIN pgmnemo.agent_lesson al ON al.id = CASE
                WHEN me.source_id = s.seed_id THEN me.target_id
                ELSE me.source_id END
            WHERE al.is_active
              AND al.id <> s.seed_id
              AND (me.valid_until IS NULL OR me.valid_until >= NOW())
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
        ),
        budget_cumsum AS (
            SELECT
                gn.id, gn.preview, gn.score, gn.chars,
                SUM(gn.chars) OVER (ORDER BY gn.score DESC ROWS UNBOUNDED PRECEDING) AS cumsum
            FROM graph_neighbors gn
        )
        SELECT
            bc.id,
            bc.preview,
            bc.score,
            LEAST(bc.cumsum, token_budget_chars)::INT,
            'graph_relation'::TEXT
        FROM budget_cumsum bc
        WHERE bc.cumsum - bc.chars < token_budget_chars;
        RETURN;
    END IF;

    -- ─────────────────────────────────────────────────────────────────────
    -- PATH D: DEFAULT — delegate to existing navigate_locate (HNSW + BM25)
    -- Used for 'lesson', 'doc', NULL content types (semantic neighborhood is real)
    -- ─────────────────────────────────────────────────────────────────────
    RETURN QUERY
    SELECT nl.id, nl.preview, nl.score, nl.tokens_consumed, nl.navigation_path
    FROM pgmnemo.navigate_locate(
        query_embedding, query_text, token_budget_chars, jsonb_filter, project_id_filter
    ) nl;
END;
$$;

COMMENT ON FUNCTION pgmnemo.navigate_locate_dispatch IS
'Per-type access-path router (§2 TIERED_MEMORY_ACCESS_MODEL): routes each query to
the cheapest adequate index for the target content_type.
  entity   → GIN BM25 on lesson_tsv (skip HNSW; navigation_path=''bm25_entity'')
  temporal → btree on t_valid_from   (skip HNSW; navigation_path=''temporal_btree'')
  relation → mem_edge BFS + BM25 seed (skip HNSW; navigation_path=''graph_relation'')
  NULL     → existing navigate_locate HNSW+BM25 unified path (fallback)
Return schema identical to navigate_locate(). Does not replace it.';

-- ============================================================================
-- 2. navigate_expand_typed — content-type-aware typed dereference (deep-dive)
-- ============================================================================
--
-- Extends navigate_expand with typed materialization per content_type:
--   'entity'  → metadata JSONB (canonical_name, entity_type, aliases) + linked lessons
--   'lesson'  → full lesson_text (same as current navigate_expand behavior)
--   'relation'→ mem_edge graph neighbors only (the relation IS the edge)
--   NULL/other→ full lesson_text (safe default matching current behavior)
--
-- Returns: id, content_type, content, typed_detail JSONB, graph_neighbor_ids, tokens_consumed, navigation_path
-- ============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.navigate_expand_typed(
    ids                     bigint[],
    graph_expand_depth      integer          DEFAULT 0,
    graph_expand_threshold  double precision DEFAULT 0.5,
    relation_types          text[]           DEFAULT NULL
)
RETURNS TABLE (
    id                      bigint,
    content_type            text,
    content                 text,
    typed_detail            jsonb,
    graph_neighbor_ids      bigint[],
    graph_neighbor_previews text[],
    tokens_consumed         integer,
    navigation_path         text
)
LANGUAGE plpgsql
STABLE PARALLEL SAFE
AS $$
BEGIN
    IF ids IS NULL OR array_length(ids, 1) IS NULL THEN
        RETURN;
    END IF;

    RETURN QUERY
    WITH seed_rows AS (
        SELECT
            al.id,
            al.content_type,
            al.lesson_text,
            al.metadata
        FROM pgmnemo.agent_lesson al
        WHERE al.id = ANY(ids)
          AND al.is_active
    ),
    -- typed_detail: content-type-specific structured projection
    typed_projection AS (
        SELECT
            sr.id,
            sr.content_type,
            -- content: what gets placed in agent context
            CASE sr.content_type
                WHEN 'entity' THEN
                    -- Entity: structured name + type, not just lesson_text
                    COALESCE(sr.metadata->>'canonical_name', sr.lesson_text)
                WHEN 'relation' THEN
                    -- Relation: brief description, graph expansion below
                    left(sr.lesson_text, 200)
                ELSE
                    -- lesson / doc / fact / NULL: full lesson_text
                    sr.lesson_text
            END::TEXT AS content,
            -- typed_detail: JSONB sidecar for structured modalities
            CASE sr.content_type
                WHEN 'entity' THEN
                    -- Entity detail: canonical metadata from JSONB
                    jsonb_build_object(
                        'canonical_name', sr.metadata->>'canonical_name',
                        'entity_type',    sr.metadata->>'entity_type',
                        'aliases',        sr.metadata->'aliases',
                        'source_ref',     sr.metadata->>'source_ref'
                    )
                WHEN 'fact' THEN
                    -- Fact detail: key-value pair from metadata
                    jsonb_build_object(
                        'key',   sr.metadata->>'key',
                        'value', sr.metadata->>'value',
                        'scope', sr.metadata->>'scope'
                    )
                ELSE NULL::JSONB
            END AS typed_detail,
            length(sr.lesson_text)                                                AS content_chars
        FROM seed_rows sr
    ),
    -- graph expansion (only when depth > 0)
    graph_neighbors AS (
        SELECT
            tp.id AS anchor_id,
            array_agg(neighbor.id)                                                AS neighbor_ids,
            array_agg(left(neighbor.lesson_text, 80))                             AS neighbor_previews
        FROM typed_projection tp
        CROSS JOIN LATERAL (
            SELECT al2.id, al2.lesson_text
            FROM pgmnemo.mem_edge me
            JOIN pgmnemo.agent_lesson al2 ON al2.id = CASE
                WHEN me.source_id = tp.id THEN me.target_id
                ELSE me.source_id END
            WHERE (me.source_id = tp.id OR me.target_id = tp.id)
              AND al2.is_active
              AND al2.id <> tp.id
              AND me.weight >= graph_expand_threshold::REAL
              AND (me.valid_until IS NULL OR me.valid_until >= NOW())
              AND (relation_types IS NULL OR me.relation_type = ANY(relation_types))
            LIMIT 20
        ) AS neighbor
        WHERE graph_expand_depth > 0
          AND (
              -- expand graph for relation content_type always (it IS the graph)
              tp.content_type = 'relation'
              -- expand entity context nodes too
              OR tp.content_type = 'entity'
              -- and honor the depth param for lessons
              OR graph_expand_depth >= 1
          )
        GROUP BY tp.id
    )
    SELECT
        tp.id,
        tp.content_type,
        tp.content,
        tp.typed_detail,
        COALESCE(gn.neighbor_ids, ARRAY[]::BIGINT[])                              AS graph_neighbor_ids,
        COALESCE(gn.neighbor_previews, ARRAY[]::TEXT[])                           AS graph_neighbor_previews,
        tp.content_chars                                                           AS tokens_consumed,
        -- navigation_path indicates which dereference was used
        CASE tp.content_type
            WHEN 'entity'   THEN 'typed_entity'
            WHEN 'fact'     THEN 'typed_fact'
            WHEN 'relation' THEN 'typed_relation'
            ELSE                 'content'
        END::TEXT                                                                  AS navigation_path
    FROM typed_projection tp
    LEFT JOIN graph_neighbors gn ON gn.anchor_id = tp.id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.navigate_expand_typed IS
'Typed dereference for the deep-dive tier (§3 TIERED_MEMORY_ACCESS_MODEL).
Each content_type gets its own materialization path:
  entity  → metadata JSONB (canonical_name, entity_type, aliases) + graph neighbors
  fact    → key/value pair from metadata JSONB + lesson_text
  relation→ lesson_text (brief) + mem_edge graph neighbors (relation IS the edge)
  lesson/NULL/other → full lesson_text (same as navigate_expand current behavior)
typed_detail JSONB sidecar carries structured data; content carries what goes in context.';

-- ============================================================================
-- 3. apply_selective_embedding_policy — selective-embedding backfill
-- ============================================================================
--
-- Sets embedding=NULL for items whose content_type does NOT require semantic embedding.
-- Non-semantic types per §5 TIERED_MEMORY_ACCESS_MODEL:
--   'entity'   → indexed by GIN/BM25 (exact name lookup); HNSW adds noise
--   'fact'     → indexed by btree/JSONB key; HNSW meaningless
--   'relation' → the edge IS the index; embedding adds nothing
--   'temporal' → indexed by btree on t_valid_from; HNSW meaningless
--
-- p_dry_run=TRUE (default): returns count without modifying data
-- p_dry_run=FALSE: executes UPDATE, returns count of rows affected
--
-- Idempotent: second run with same policy changes 0 rows (already NULL).
-- ============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.apply_selective_embedding_policy(
    p_dry_run  boolean DEFAULT TRUE
)
RETURNS TABLE (
    affected_count  bigint,
    by_content_type jsonb,
    dry_run         boolean
)
LANGUAGE plpgsql
AS $$
DECLARE
    _non_semantic_types TEXT[] := ARRAY['entity', 'fact', 'relation', 'temporal'];
    _counts JSONB;
    _total  BIGINT;
BEGIN
    -- Count by type first (always)
    SELECT
        jsonb_object_agg(
            COALESCE(sub.content_type, 'NULL'),
            cnt
        ),
        SUM(cnt)
    INTO _counts, _total
    FROM (
        SELECT content_type, COUNT(*) AS cnt
        FROM pgmnemo.agent_lesson
        WHERE is_active = TRUE
          AND content_type = ANY(_non_semantic_types)
          AND embedding IS NOT NULL   -- only those currently embedded
        GROUP BY content_type
    ) sub;

    IF NOT p_dry_run THEN
        -- Apply: set embedding=NULL for non-semantic types
        UPDATE pgmnemo.agent_lesson
        SET
            embedding    = NULL,
            updated_at   = NOW()
        WHERE is_active = TRUE
          AND content_type = ANY(_non_semantic_types)
          AND embedding IS NOT NULL;
        -- _total is exact because we checked IS NOT NULL above
    END IF;

    RETURN QUERY SELECT
        COALESCE(_total, 0)   AS affected_count,
        COALESCE(_counts, '{}'::JSONB) AS by_content_type,
        p_dry_run             AS dry_run;
END;
$$;

COMMENT ON FUNCTION pgmnemo.apply_selective_embedding_policy IS
'Selective-embedding backfill per §5 TIERED_MEMORY_ACCESS_MODEL.
Sets embedding=NULL for non-semantic content types (entity, fact, relation, temporal)
so they are indexed via GIN/btree/graph rather than HNSW. Reduces embedding index size
and removes noise from HNSW neighborhood for semantic (lesson/doc) queries.
p_dry_run=TRUE (default): preview only. p_dry_run=FALSE: executes update.
Run AFTER navigate_locate_dispatch is deployed so non-semantic types are still findable.';

-- Update extension version
-- (version number managed by pgmnemo.control; this script is the canonical change record)

-- ============================================================================
-- 4. recall_fast — HNSW-only vector recall (O(k log n), no BM25/graph/RRF)
-- ============================================================================
--
-- Fast path for latency-sensitive MCP recall. Uses HNSW ORDER BY <=> LIMIT k
-- directly — no BM25 TSV scoring, no graph BFS, no RRF, no recency weighting.
-- Returns pure cosine similarity as score.
--
-- Filters: role_filter / project_id_filter / exclude_dag_id (same as recall_hybrid).
-- Stamping: last_recalled_at + recall_count updated iff track_recall_recency = on.
-- Return shape: 12-column — identical to recall_lessons() (MCP-compatible).
--
-- Use recall_fast() as the default MCP recall path.
-- Use recall_hybrid() (deep=true in MCP) when BM25 + graph fusion is required.
-- ============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.recall_fast(
    query_embedding   vector(1024),
    k                 INT     DEFAULT 10,
    role_filter       TEXT    DEFAULT NULL,
    project_id_filter INT     DEFAULT NULL,
    exclude_dag_id    TEXT    DEFAULT NULL
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _track_recency      BOOLEAN;
BEGIN
    -- Set HNSW ef_search from GUC (same pattern as recall_hybrid)
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _track_recency := COALESCE(
            NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN, TRUE);
    EXCEPTION WHEN OTHERS THEN _track_recency := TRUE;
    END;

    -- #84: reject NULL query_embedding early — HNSW-only path has no text fallback.
    -- recall_hybrid() accepts NULL query_embedding when query_text is present; recall_fast
    -- is vector-only and cannot fall back to BM25, so NULL embedding is always an error.
    IF query_embedding IS NULL THEN
        RAISE EXCEPTION
            'pgmnemo.recall_fast: query_embedding IS NULL -- '
            'a vector embedding is required for HNSW search. '
            'recall_fast has no text-only fallback; use recall_hybrid() '
            'if you have query_text but no embedding.';
    END IF;

    RETURN QUERY
    WITH fast_ranked AS (
        SELECT
            al.id,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS vec_score,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_fast.role_filter IS NULL
               OR al.role = recall_fast.role_filter)
          AND (recall_fast.project_id_filter IS NULL
               OR al.project_id = recall_fast.project_id_filter)
          AND (recall_fast.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_fast.exclude_dag_id)
        ORDER BY al.embedding <=> query_embedding
        LIMIT k
    ),
    stamped AS (
        UPDATE pgmnemo.agent_lesson al2
        SET
            last_recalled_at = NOW(),
            recall_count     = al2.recall_count + 1
        FROM fast_ranked fr
        WHERE al2.id = fr.id
          AND _track_recency
        RETURNING al2.id
    )
    SELECT
        fr.id          AS lesson_id,
        fr.vec_score   AS score,
        fr.role,
        fr.project_id,
        fr.topic,
        fr.lesson_text,
        fr.importance,
        fr.metadata,
        fr.commit_sha,
        fr.artifact_hash,
        fr.verified_at,
        fr.created_at
    FROM fast_ranked fr
    -- stamped CTE is a side-effect sink; reference it to prevent optimiser elision
    LEFT JOIN stamped s ON s.id = fr.id
    ORDER BY fr.vec_score DESC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_fast(vector, INT, TEXT, INT, TEXT) IS
    'HNSW-only vector recall — O(k log n), no BM25/graph/RRF. '
    'Uses ORDER BY embedding <=> query LIMIT k to activate the HNSW index. '
    'score = cosine similarity (1 - distance). '
    'Respects include_unverified, ef_search, track_recall_recency GUCs. '
    'Filters: role_filter, project_id_filter, exclude_dag_id (same as recall_hybrid). '
    'v0.10.0: default MCP recall path. Use recall_hybrid for full 6-signal fusion. '
    'v0.10.1 #84: raises EXCEPTION when query_embedding IS NULL (no text-only fallback).';


-- ─────────────────────────────────────────────────────────────────────────────
-- v0.10.1 upgrade applied inline (pgmnemo--0.10.0--0.10.1.sql)
-- ─────────────────────────────────────────────────────────────────────────────

-- A. Schema: tsvector columns already use 'simple' in CREATE TABLE above —
--    no ALTER needed on a fresh install (PG14-16-safe).

-- Update the legacy trigger function (v0.2.x compat shim) to match.
-- The trigger is a no-op in PG 17 because lesson_tsv is GENERATED ALWAYS,
-- but update it to avoid confusing 'english' vs 'simple' in code.
CREATE OR REPLACE FUNCTION pgmnemo._update_lesson_tsv() RETURNS TRIGGER AS $$
BEGIN
    -- Note: lesson_tsv is GENERATED ALWAYS — this assignment is silently ignored
    -- by the PG executor. The trigger remains for upgrade-path compat with pre-0.9.x
    -- installations where lesson_tsv was a plain column.
    NEW.lesson_tsv := to_tsvector('simple', COALESCE(NEW.lesson_text, ''));
    RETURN NEW;
END;
$$ LANGUAGE plpgsql;

-- ─────────────────────────────────────────────────────────────────────────────
-- B. recall_hybrid — v0.10.1 (#87: all four fixes)
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60,
    exclude_dag_id    TEXT             DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;
    _fetch_k_vec        INT;
    _fetch_k_bm25       INT;
    _conf_boost_w       DOUBLE PRECISION;
    -- v0.10.1 / issue #87 additions
    _lexical_text       TEXT;               -- Fix 1: capped query_text for BM25 (≤200 chars)
    _bm25_budget_ms     INT;                -- Fix 3: per-signal time budget for BM25
    _bm25_timed_out     BOOLEAN := FALSE;   -- Fix 3: graceful-degradation flag
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    BEGIN
        _conf_boost_w := GREATEST(0.0, LEAST(0.01, COALESCE(
            NULLIF(current_setting('pgmnemo.confidence_boost_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));
    EXCEPTION WHEN OTHERS THEN _conf_boost_w := 0.0;
    END;

    -- Fix 3: read per-signal BM25 time budget (GUC pgmnemo.bm25_budget_ms, default 250 ms)
    BEGIN
        _bm25_budget_ms := GREATEST(50, COALESCE(
            NULLIF(current_setting('pgmnemo.bm25_budget_ms', TRUE), '')::INT, 250));
    EXCEPTION WHEN OTHERS THEN _bm25_budget_ms := 250;
    END;

    -- Fix 1 + Fix 4: cap query_text to 200 chars for lexical path; use 'simple' tsconfig.
    -- The embedding carries full semantics; BM25 only needs salient head tokens.
    IF _has_text THEN
        _lexical_text := left(trim(query_text), 200);   -- Fix 1: ≤200 chars
        BEGIN
            _tsquery := websearch_to_tsquery('simple', _lexical_text);   -- Fix 4
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', _lexical_text);    -- Fix 4 fallback
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    -- ── Phase 2: BM25 with per-signal time budget (Fix 3) ──────────────────────
    -- BM25 results land in session-level temp table _pgmnemo_bm25_work.
    -- ON COMMIT DROP scopes it to the current transaction; duplicate_table on
    -- re-entrant calls falls back to TRUNCATE.
    -- When the INSERT exceeds _bm25_budget_ms, PG's implicit savepoint rolls it
    -- back (leaving the temp table empty) and reverts statement_timeout to its
    -- pre-block value.  Function continues with vector-only recall — no error.
    BEGIN
        CREATE TEMP TABLE _pgmnemo_bm25_work (
            id             BIGINT         PRIMARY KEY,
            raw_bm25_score DOUBLE PRECISION NOT NULL DEFAULT 0.0
        ) ON COMMIT DROP;
    EXCEPTION WHEN duplicate_table THEN
        TRUNCATE TABLE _pgmnemo_bm25_work;
    END;

    IF _has_text THEN
        BEGIN
            -- statement_timeout is savepoint-scoped; auto-reverts if exception fires.
            EXECUTE format('SET LOCAL statement_timeout = %s', _bm25_budget_ms);

            INSERT INTO _pgmnemo_bm25_work (id, raw_bm25_score)
            SELECT
                al.id,
                -- Fix 2: use pre-computed indexed full_text (no per-row to_tsvector(topic))
                ts_rank_cd(al.full_text, _tsquery, 32)::DOUBLE PRECISION
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              -- Fix 2: al.full_text @@ _tsquery uses the GIN index on full_text
              AND al.full_text @@ _tsquery
              AND (_include_unverified OR al.verified_at IS NOT NULL)
              AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
              AND (recall_hybrid.project_id_filter IS NULL
                   OR al.project_id = recall_hybrid.project_id_filter)
              AND (recall_hybrid.exclude_dag_id IS NULL
                   OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
              AND (_as_of_ts IS NULL
                   OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
              AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
            ORDER BY 2 DESC
            LIMIT _fetch_k_bm25;

            -- Reset to no-timeout on success (restores full time budget for RETURN QUERY).
            EXECUTE 'SET LOCAL statement_timeout = 0';

        EXCEPTION WHEN query_canceled THEN  -- catches both query_cancel and statement_timeout (both SQLSTATE 57014)
            -- Savepoint rolled back: INSERT undone, _pgmnemo_bm25_work is empty,
            -- statement_timeout reverted to pre-block value — no manual cleanup needed.
            _bm25_timed_out := TRUE;
            _has_text       := FALSE;
            RAISE NOTICE
                'pgmnemo.recall_hybrid: BM25 signal exceeded %ms budget — degrading to '
                'vector-only recall. Tune pgmnemo.bm25_budget_ms or shorten query_text.',
                _bm25_budget_ms;
        END;
    END IF;

    -- ── Phase 3+4: vec + BM25 fusion → RRF → graph proximity → final scoring ───
    RETURN QUERY
    WITH RECURSIVE
    -- Phase 1: HNSW vector retrieval (index scan — always runs)
    vec_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS raw_vec_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_vec
          AND al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (recall_hybrid.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY al.embedding <=> query_embedding
        LIMIT _fetch_k_vec
    ),
    -- Merge: vec candidates with BM25 scores (left join from temp table),
    -- plus BM25-only candidates (join back to agent_lesson for full row data).
    all_candidates AS (
        -- Vector candidates with BM25 score where available (may be 0 if no BM25 match)
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(bw.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM vec_candidates v
        LEFT JOIN _pgmnemo_bm25_work bw ON bw.id = v.id

        UNION ALL

        -- BM25-only candidates (not in vec set) — join agent_lesson for full row
        SELECT
            al.id, al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            bw.raw_bm25_score
        FROM _pgmnemo_bm25_work bw
        JOIN pgmnemo.agent_lesson al ON al.id = bw.id
        WHERE bw.id NOT IN (SELECT id FROM vec_candidates)
    ),
    -- RRF ranking over bounded candidate set
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC) AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            -- v0.8.1 F2: ordinal RRF + cardinal raw score blend
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    -- graph_walk is inherently bounded (5 anchors × depth 5 = ≤25 edge hops)
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
              -- I1: additive zero-centered confidence boost (w=0 when GUC off → no effect)
              + _conf_boost_w * (s.confidence::DOUBLE PRECISION - 0.5)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    ),
    -- v0.9.5: materialise top-k before stamping
    final_results AS MATERIALIZED (
        SELECT
            f.id                   AS lesson_id,
            f.final_score          AS score,
            f.v_score              AS vec_score,
            f.b_score              AS bm25_score,
            f.rrf_sparse           AS rrf_score,
            f.role,
            f.project_id,
            f.topic,
            f.lesson_text,
            f.importance,
            f.metadata,
            f.commit_sha,
            f.artifact_hash,
            f.verified_at,
            f.created_at,
            f.confidence::REAL,
            LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
        FROM final f
        ORDER BY f.final_score DESC, f.id ASC
        LIMIT k
    ),
    -- v0.9.5: stamp recency on returned lessons (GUC-gated)
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT lesson_id FROM final_results))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT
        fr.lesson_id,
        fr.score,
        fr.vec_score,
        fr.bm25_score,
        fr.rrf_score,
        fr.role,
        fr.project_id,
        fr.topic,
        fr.lesson_text,
        fr.importance,
        fr.metadata,
        fr.commit_sha,
        fr.artifact_hash,
        fr.verified_at,
        fr.created_at,
        fr.confidence,
        fr.match_confidence
    FROM final_results fr
    ORDER BY fr.score DESC, fr.lesson_id ASC;

    -- F2: ghost guidance — if 0 rows returned, check for unverified (ghost) lessons in scope
    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT, TEXT) IS
    'v0.10.1 — #87: recall robustness. '
    'Fix 1: query_text capped to 200 chars for BM25 (embedding carries full semantics). '
    'Fix 2: BM25 WHERE uses indexed full_text @@ _tsquery — no per-row to_tsvector(topic). '
    'Fix 3: BM25 runs in isolated exception block with SET LOCAL statement_timeout = pgmnemo.bm25_budget_ms '
    '(default 250 ms). On timeout: graceful degradation to vector-only (no error). '
    'Fix 4: tsconfig changed from english to simple for RU/EN/code mixed corpus. '
    'v0.9.6 — R13: exclude_dag_id TEXT DEFAULT NULL. '
    'v0.9.5 — recall-recency stamping (last_recalled_at, recall_count). '
    'v0.9.2 — I1: confidence-weighted ranking (pgmnemo.confidence_boost_weight). '
    'v0.8.2 — F2: ghost-count NOTICE when 0 rows. '
    'VOLATILE (side-effects: recency stamp, temp table _pgmnemo_bm25_work).';

-- ─────────────────────────────────────────────────────────────────────────────
-- C. recall_lessons — update tsconfig to 'simple' (vector-only fallback path)
--    Hybrid path delegates to recall_hybrid; this affects the non-hybrid branch.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT         DEFAULT 10,
    role_filter       TEXT        DEFAULT NULL,
    project_id_filter INT         DEFAULT NULL,
    query_text        TEXT        DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ DEFAULT NULL,
    exclude_dag_id    TEXT        DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
    _ghost_count        INT;
BEGIN
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT, 2000);
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars. Original: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    _has_vec  := query_embedding IS NOT NULL;
    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _disable_hybrid := FALSE;
    END;

    -- Hybrid path delegates to recall_hybrid (which handles stamping + exclude_dag_id)
    IF NOT _disable_hybrid AND _has_vec AND _has_text THEN
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        RETURN QUERY
        SELECT
            h.lesson_id, h.score, h.role, h.project_id, h.topic, h.lesson_text,
            h.importance, h.metadata, h.commit_sha, h.artifact_hash,
            h.verified_at, h.created_at,
            h.vec_score, h.bm25_score, h.rrf_score,
            h.confidence, h.match_confidence
        FROM pgmnemo.recall_hybrid(
            query_embedding, _query_text, k,
            role_filter, project_id_filter, 0.4, 0.4, 60,
            exclude_dag_id
        ) h;
        RETURN;
    END IF;

    -- Vector-only path (pgmnemo.disable_hybrid = 'true' or no query_text)
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05);
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION, 1.0)));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('simple', left(trim(_query_text), 200));  -- Fix 4+1
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', left(trim(_query_text), 200));   -- Fix 4+1
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at,
            al.confidence,
            CASE
                WHEN al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery     -- Fix 2: indexed full_text
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL
               OR al.project_id = recall_lessons.project_id_filter)
          AND (recall_lessons.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_lessons.exclude_dag_id)
          AND (al.embedding IS NOT NULL OR _has_text)
    ),
    anchors AS (
        SELECT id FROM candidates ORDER BY vec_score DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    scored AS (
        SELECT
            c.id, c.role, c.project_id, c.topic, c.lesson_text, c.importance,
            c.metadata, c.commit_sha, c.artifact_hash, c.verified_at, c.created_at,
            c.confidence,
            c.vec_score,
            c.ft_score,
            (c.vec_score + _gamma * GREATEST(0.0, 1.0 - LEAST(
                 EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0), 1.0
             ))) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
             + c.ft_score * 0.1
             AS combined_score
        FROM candidates c
        LEFT JOIN graph_proximity gp ON gp.lesson_id = c.id
    )
    SELECT
        s.id                  AS lesson_id,
        s.combined_score      AS score,
        s.role,
        s.project_id,
        s.topic,
        s.lesson_text,
        s.importance,
        s.metadata,
        s.commit_sha,
        s.artifact_hash,
        s.verified_at,
        s.created_at,
        s.vec_score,
        s.ft_score            AS bm25_score,
        0.0::DOUBLE PRECISION AS rrf_score,
        s.confidence::REAL,
        LEAST(1.0, GREATEST(0.0, s.vec_score))::REAL AS match_confidence
    FROM scored s
    ORDER BY s.combined_score DESC, s.id ASC
    LIMIT k;

    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active AND al.t_valid_to = 'infinity'::TIMESTAMPTZ AND al.verified_at IS NULL
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL
               OR al.project_id = recall_lessons.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % unverified lesson(s) excluded. '
                'SET pgmnemo.include_unverified = ''on'' to include them.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

-- ─────────────────────────────────────────────────────────────────────────────
-- D. navigate_locate — update tsconfig to 'simple'
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.navigate_locate(
    query_embedding   vector(1024),
    query_text        TEXT,
    token_budget_chars INT              DEFAULT 2000,
    jsonb_filter      JSONB             DEFAULT NULL,
    project_id_filter INT               DEFAULT NULL
)
RETURNS TABLE (
    id              BIGINT,
    preview         TEXT,
    score           FLOAT8,
    tokens_consumed INT,
    navigation_path TEXT
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 2;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _vec_weight         CONSTANT DOUBLE PRECISION := 0.4;
    _bm25_weight        CONSTANT DOUBLE PRECISION := 0.4;
    _raw_blend_weight   DOUBLE PRECISION;
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;
    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION 'pgmnemo.navigate_locate: both query_embedding and query_text are NULL/empty';
    END IF;

    _rrf_k_f := 60.0;
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100
        );
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE
        );
    EXCEPTION WHEN OTHERS THEN
        _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN
        _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2
        );
    EXCEPTION WHEN OTHERS THEN
        _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;
    _graph_weight := GREATEST(0.0, LEAST(0.5, _graph_weight));

    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('simple', left(trim(query_text), 200));  -- Fix 4+1
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', left(trim(query_text), 200));   -- Fix 4+1
            EXCEPTION WHEN OTHERS THEN
                _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    raw_candidates AS (
        SELECT
            al.id,
            al.topic_tsv,
            al.lesson_tsv,
            al.lesson_text,
            al.importance,
            al.commit_sha,
            al.verified_at,
            al.created_at,
            al.metadata,
            length(al.lesson_text)                                            AS text_len,
            CASE
                WHEN _has_vec AND al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_vec_score,
            CASE
                WHEN _has_text AND (al.topic_tsv @@ _tsquery OR al.lesson_tsv @@ _tsquery)
                THEN ts_rank_cd(
                    setweight(al.topic_tsv, 'A') || al.lesson_tsv,
                    _tsquery, 32)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS raw_bm25_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (navigate_locate.project_id_filter IS NULL
               OR al.project_id = navigate_locate.project_id_filter)
          AND (navigate_locate.jsonb_filter IS NULL
               OR al.metadata @> navigate_locate.jsonb_filter)
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
          AND (
                  (_has_vec  AND al.embedding IS NOT NULL)
               OR (_has_text AND (al.topic_tsv @@ _tsquery OR al.lesson_tsv @@ _tsquery))
          )
    ),
    rrf_ranked AS (
        SELECT *,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC)  AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK()   OVER (PARTITION BY (raw_bm25_score > 0)
                                     ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                                  AS bm25_rank_sparse,
            COUNT(*) OVER ()                                                     AS n_candidates
        FROM raw_candidates
    ),
    scored AS (
        SELECT
            r.id, r.text_len, r.lesson_text, r.metadata, r.importance,
            r.commit_sha, r.verified_at, r.created_at,
            r.vec_rank, r.n_candidates,
            CASE WHEN r.bm25_rank_sparse IS NOT NULL THEN r.bm25_rank_sparse
                 ELSE r.n_candidates + 1
            END AS bm25_rank_eff,
            (
                _vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
              + _bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                     r.n_candidates + 1)::DOUBLE PRECISION)
              + _raw_blend_weight * (
                    _vec_weight  * r.raw_vec_score
                  + _bm25_weight * r.raw_bm25_score)
            ) AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT
            gw.anchor_id,
            gw.depth + 1,
            CASE WHEN me.source_id = gw.reached_id
                    THEN me.target_id
                    ELSE me.source_id
               END
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON (
            me.source_id = gw.reached_id OR me.target_id = gw.reached_id
        )
        WHERE gw.depth < _max_depth
          AND (me.valid_until IS NULL OR me.valid_until = 'infinity'::TIMESTAMPTZ)
    ),
    graph_proximity AS (
        SELECT
            gw.reached_id AS lesson_id,
            MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw
        WHERE gw.depth > 0
        GROUP BY gw.reached_id
    ),
    final_ranked AS (
        SELECT
            s.id,
            s.text_len,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.05 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.05 * GREATEST(0.0,
                               1.0 - LEAST(
                                   EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0),
                                   1.0))::DOUBLE PRECISION
                  + 0.05 * (CASE
                              WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                              WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                              ELSE 0.0 END)::DOUBLE PRECISION
                )
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.lesson_text,
            s.metadata
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    ),
    budget_consumed AS (
        SELECT
            fr.id,
            fr.final_score,
            fr.lesson_text,
            fr.metadata,
            fr.text_len,
            SUM(fr.text_len) OVER (ORDER BY fr.final_score DESC, fr.id ASC) AS cumulative_chars
        FROM final_ranked fr
    )
    SELECT
        bc.id,
        left(bc.lesson_text, 120)::TEXT  AS preview,
        bc.final_score                   AS score,
        bc.text_len::INT                 AS tokens_consumed,
        NULL::TEXT                       AS navigation_path
    FROM budget_consumed bc
    WHERE bc.cumulative_chars <= navigate_locate.token_budget_chars
    ORDER BY bc.final_score DESC, bc.id ASC;
END;
$$;

-- ─────────────────────────────────────────────────────────────────────────────
-- E. navigate_locate_dispatch — update tsconfig to 'simple'
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.navigate_locate_dispatch(
    query_embedding   vector(1024)  DEFAULT NULL,
    query_text        TEXT          DEFAULT NULL,
    content_type_dispatch TEXT      DEFAULT NULL,
    project_id_filter INT           DEFAULT NULL,
    jsonb_filter      JSONB         DEFAULT NULL,
    token_budget_chars INT          DEFAULT 2000
)
RETURNS TABLE (
    id              BIGINT,
    preview         TEXT,
    score           FLOAT8,
    tokens_consumed INT,
    navigation_path TEXT
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _tsquery TSQUERY;
    _has_text BOOLEAN;
    _has_vec  BOOLEAN;
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    -- PATH A: ENTITY DISPATCH — GIN BM25, no HNSW
    IF content_type_dispatch = 'entity' THEN
        IF NOT _has_text THEN
            RAISE EXCEPTION 'pgmnemo.navigate_locate_dispatch: entity dispatch requires non-empty query_text';
        END IF;
        BEGIN
            _tsquery := websearch_to_tsquery('simple', left(trim(query_text), 200));  -- Fix 4+1
        EXCEPTION WHEN OTHERS THEN
            _tsquery := plainto_tsquery('simple', left(trim(query_text), 200));        -- Fix 4+1
        END;

        RETURN QUERY
        WITH entity_candidates AS (
            SELECT
                al.id,
                left(al.lesson_text, 120)::TEXT                                  AS preview,
                ts_rank_cd(
                    setweight(al.topic_tsv, 'A') || al.lesson_tsv,
                    _tsquery, 32
                )::DOUBLE PRECISION                                               AS score,
                length(al.lesson_text)                                           AS chars
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND al.content_type = 'entity'
              AND (al.lesson_tsv @@ _tsquery OR al.topic_tsv @@ _tsquery)
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
              AND (jsonb_filter IS NULL OR al.metadata @> jsonb_filter)
              AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
        )
        SELECT
            ec.id,
            ec.preview,
            ec.score,
            ec.chars::INT AS tokens_consumed,
            'entity'::TEXT AS navigation_path
        FROM entity_candidates ec
        WHERE ec.score > 0
        ORDER BY ec.score DESC
        LIMIT 20;
        RETURN;
    END IF;

    -- PATH B: RELATION DISPATCH — graph seed via BM25 + 1-hop BFS
    IF content_type_dispatch = 'relation' THEN
        IF NOT _has_text THEN
            RAISE EXCEPTION 'pgmnemo.navigate_locate_dispatch: relation dispatch requires non-empty query_text';
        END IF;
        BEGIN
            _tsquery := websearch_to_tsquery('simple', left(trim(query_text), 200));  -- Fix 4+1
        EXCEPTION WHEN OTHERS THEN
            _tsquery := plainto_tsquery('simple', left(trim(query_text), 200));        -- Fix 4+1
        END;

        RETURN QUERY
        WITH seed_ids AS (
            SELECT al.id AS seed_id, ts_rank_cd(al.lesson_tsv, _tsquery, 32)::DOUBLE PRECISION AS seed_score
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND (al.lesson_tsv @@ _tsquery OR al.topic_tsv @@ _tsquery)
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
            ORDER BY seed_score DESC
            LIMIT 10
        ),
        graph_neighbors AS (
            SELECT DISTINCT
                al.id,
                left(al.lesson_text, 120)::TEXT                                  AS preview,
                (me.weight * 0.7 + s.seed_score * 0.3)::DOUBLE PRECISION        AS score,
                length(al.lesson_text)                                           AS chars
            FROM seed_ids s
            JOIN pgmnemo.mem_edge me ON (me.source_id = s.seed_id OR me.target_id = s.seed_id)
            JOIN pgmnemo.agent_lesson al ON al.id = CASE
                WHEN me.source_id = s.seed_id THEN me.target_id
                ELSE me.source_id END
            WHERE al.is_active
              AND (project_id_filter IS NULL OR al.project_id = project_id_filter)
        )
        SELECT
            gn.id,
            gn.preview,
            gn.score,
            gn.chars::INT AS tokens_consumed,
            'relation'::TEXT AS navigation_path
        FROM graph_neighbors gn
        ORDER BY gn.score DESC
        LIMIT 20;
        RETURN;
    END IF;

    -- PATH C: DEFAULT — delegate to navigate_locate (handles NULL content_type + mixed signals)
    RETURN QUERY
    SELECT nl.id, nl.preview, nl.score, nl.tokens_consumed, nl.navigation_path
    FROM pgmnemo.navigate_locate(
        query_embedding, query_text,
        token_budget_chars, jsonb_filter, project_id_filter
    ) nl;
END;
$$;

-- ─────────────────────────────────────────────────────────────────────────────
-- F. Update COMMENTs to reflect version bump
-- ─────────────────────────────────────────────────────────────────────────────

COMMENT ON COLUMN pgmnemo.agent_lesson.topic_tsv IS
    'Stored tsvector for topic column — tsconfig ''simple'' (v0.10.1, Fix 4). '
    'GIN index: pgmnemo_agent_lesson_topic_tsv_idx.';

COMMENT ON COLUMN pgmnemo.agent_lesson.lesson_tsv IS
    'Stored tsvector for lesson_text — tsconfig ''simple'' (v0.10.1, Fix 4). '
    'GIN index: pgmnemo_agent_lesson_lesson_tsv_idx.';

COMMENT ON COLUMN pgmnemo.agent_lesson.full_text IS
    'Weighted tsvector: topic (weight A) || lesson_text (weight B), tsconfig ''simple'' (v0.10.1, Fix 4). '
    'Used by recall_hybrid BM25 phase (Fix 2) and recall_lessons full_text path. '
    'GIN index: pgmnemo_agent_lesson_full_text_idx.';

-- ─────────────────────────────────────────────────────────────────────────────
-- v0.11.0 additions (typed recall — RFC-001 §D2 / P0.2)
-- ─────────────────────────────────────────────────────────────────────────────
-- ─────────────────────────────────────────────────────────────────────────────
-- recall_hybrid — v0.11.0 (based on 0.10.1 body + p_content_types param)
--
-- Drop old 9-param overload (from 0.10.1) to prevent ambiguous-function errors.
-- The 10-param version is backward-compatible: p_content_types DEFAULT NULL.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_hybrid(
    vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT, TEXT
);

CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60,
    exclude_dag_id    TEXT             DEFAULT NULL,
    p_content_types   text[]           DEFAULT NULL   -- P0.2: typed recall; NULL=all types
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;
    _fetch_k_vec        INT;
    _fetch_k_bm25       INT;
    _conf_boost_w       DOUBLE PRECISION;
    -- 0.10.1 additions (#87)
    _lexical_text       TEXT;
    _bm25_budget_ms     INT;
    _bm25_timed_out     BOOLEAN := FALSE;
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2)));
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.2;
    END;

    BEGIN
        _conf_boost_w := GREATEST(0.0, LEAST(0.01, COALESCE(
            NULLIF(current_setting('pgmnemo.confidence_boost_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));
    EXCEPTION WHEN OTHERS THEN _conf_boost_w := 0.0;
    END;

    BEGIN
        _bm25_budget_ms := GREATEST(1, COALESCE(
            NULLIF(current_setting('pgmnemo.bm25_budget_ms', TRUE), '')::INT, 250));
    EXCEPTION WHEN OTHERS THEN _bm25_budget_ms := 250;
    END;

    IF _has_text THEN
        _lexical_text := left(trim(query_text), 200);
        BEGIN
            _tsquery := websearch_to_tsquery('simple', _lexical_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', _lexical_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    BEGIN
        CREATE TEMP TABLE _pgmnemo_bm25_work (
            id             BIGINT         PRIMARY KEY,
            raw_bm25_score DOUBLE PRECISION NOT NULL DEFAULT 0.0
        ) ON COMMIT DROP;
    EXCEPTION WHEN duplicate_table THEN
        TRUNCATE TABLE _pgmnemo_bm25_work;
    END;

    IF _has_text THEN
        BEGIN
            EXECUTE format('SET LOCAL statement_timeout = %s', _bm25_budget_ms);

            INSERT INTO _pgmnemo_bm25_work (id, raw_bm25_score)
            SELECT
                al.id,
                ts_rank_cd(al.full_text, _tsquery, 32)::DOUBLE PRECISION
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND al.full_text @@ _tsquery
              AND (_include_unverified OR al.verified_at IS NOT NULL)
              AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
              AND (recall_hybrid.project_id_filter IS NULL
                   OR al.project_id = recall_hybrid.project_id_filter)
              AND (recall_hybrid.exclude_dag_id IS NULL
                   OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
              -- P0.2: typed recall pushdown into BM25 subplan (ix_pgmnemo_content_type_active)
              AND (recall_hybrid.p_content_types IS NULL
                   OR al.content_type = ANY(recall_hybrid.p_content_types))
              AND (_as_of_ts IS NULL
                   OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
              AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
            ORDER BY 2 DESC
            LIMIT _fetch_k_bm25;

            EXECUTE 'SET LOCAL statement_timeout = 0';

        EXCEPTION WHEN query_canceled THEN
            _bm25_timed_out := TRUE;
            _has_text       := FALSE;
            RAISE NOTICE
                'pgmnemo.recall_hybrid: BM25 signal exceeded %ms budget — degrading to '
                'vector-only recall. Tune pgmnemo.bm25_budget_ms or shorten query_text.',
                _bm25_budget_ms;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    vec_candidates AS (
        SELECT
            al.id,
            al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS raw_vec_score
        FROM pgmnemo.agent_lesson al
        WHERE _has_vec
          AND al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter)
          AND (recall_hybrid.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
          -- P0.2: typed recall pushdown into vector subplan (ix_pgmnemo_content_type_active)
          AND (recall_hybrid.p_content_types IS NULL
               OR al.content_type = ANY(recall_hybrid.p_content_types))
          AND (_as_of_ts IS NULL
               OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
          AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
        ORDER BY al.embedding <=> query_embedding
        LIMIT _fetch_k_vec
    ),
    all_candidates AS (
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(bw.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM vec_candidates v
        LEFT JOIN _pgmnemo_bm25_work bw ON bw.id = v.id

        UNION ALL

        SELECT
            al.id, al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            bw.raw_bm25_score
        FROM _pgmnemo_bm25_work bw
        JOIN pgmnemo.agent_lesson al ON al.id = bw.id
        WHERE bw.id NOT IN (SELECT id FROM vec_candidates)
    ),
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC) AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
              + _conf_boost_w * (s.confidence::DOUBLE PRECISION - 0.5)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    ),
    final_results AS MATERIALIZED (
        SELECT
            f.id                   AS lesson_id,
            f.final_score          AS score,
            f.v_score              AS vec_score,
            f.b_score              AS bm25_score,
            f.rrf_sparse           AS rrf_score,
            f.role,
            f.project_id,
            f.topic,
            f.lesson_text,
            f.importance,
            f.metadata,
            f.commit_sha,
            f.artifact_hash,
            f.verified_at,
            f.created_at,
            f.confidence::REAL,
            LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
        FROM final f
        ORDER BY f.final_score DESC, f.id ASC
        LIMIT k
    ),
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT lesson_id FROM final_results))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT
        fr.lesson_id, fr.score, fr.vec_score, fr.bm25_score, fr.rrf_score,
        fr.role, fr.project_id, fr.topic, fr.lesson_text, fr.importance,
        fr.metadata, fr.commit_sha, fr.artifact_hash, fr.verified_at, fr.created_at,
        fr.confidence, fr.match_confidence
    FROM final_results fr
    ORDER BY fr.score DESC, fr.lesson_id ASC;

    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT, TEXT, text[]) IS
    'v0.11.0 — RFC-001 §D2 / P0.2: typed recall. '
    'New param p_content_types text[] DEFAULT NULL (LAST, backward-compatible). '
    'NULL → unchanged behavior (all content types). '
    'non-NULL → pushes content_type = ANY(p_content_types) into BOTH subplans (vec + BM25) '
    'BEFORE RRF fusion — uses ix_pgmnemo_content_type_active (pushdown, not post-filter). '
    'Empty array ''{}'': zero rows returned (no silent fallback to all-types). '
    'Inherits all v0.10.1 (#87) fixes: query_text cap, indexed full_text BM25, '
    'bm25_budget_ms timeout, simple tsconfig. '
    'match_confidence: vec_score (cosine similarity, [0,1]). '
    'graph_proximity via mem_edge causal/temporal walk (depth ≤5). '
    'VOLATILE (side-effects: recency stamp, temp table _pgmnemo_bm25_work).';

-- ═══════════════════════════════════════════════════════════════════════════
-- pgmnemo 0.11.0 → 0.11.1 delta applied inline (typed recall hot-path)
-- ═══════════════════════════════════════════════════════════════════════════

-- ─────────────────────────────────────────────────────────────────────────────
-- 1. recall_fast() — 5 → 6 params: add p_content_types TEXT[] DEFAULT NULL
--
-- Drop the old 5-param overload to prevent ambiguous-function errors when
-- callers pass exactly 5 positional arguments. The 6-param version is
-- backward-compatible: existing 5-arg call sites pick up the DEFAULT NULL.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_fast(vector, INT, TEXT, INT, TEXT);

CREATE OR REPLACE FUNCTION pgmnemo.recall_fast(
    query_embedding   vector(1024),
    k                 INT     DEFAULT 10,
    role_filter       TEXT    DEFAULT NULL,
    project_id_filter INT     DEFAULT NULL,
    exclude_dag_id    TEXT    DEFAULT NULL,
    p_content_types   TEXT[]  DEFAULT NULL   -- P0.2: typed recall; NULL=all types
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _track_recency      BOOLEAN;
BEGIN
    -- Set HNSW ef_search from GUC (same pattern as recall_hybrid)
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _track_recency := COALESCE(
            NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN, TRUE);
    EXCEPTION WHEN OTHERS THEN _track_recency := TRUE;
    END;

    -- #84: reject NULL query_embedding early — HNSW-only path has no text fallback.
    -- recall_hybrid() accepts NULL query_embedding when query_text is present; recall_fast
    -- is vector-only and cannot fall back to BM25, so NULL embedding is always an error.
    IF query_embedding IS NULL THEN
        RAISE EXCEPTION
            'pgmnemo.recall_fast: query_embedding IS NULL -- '
            'a vector embedding is required for HNSW search. '
            'recall_fast has no text-only fallback; use recall_hybrid() '
            'if you have query_text but no embedding.';
    END IF;

    RETURN QUERY
    WITH fast_ranked AS (
        SELECT
            al.id,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS vec_score,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_fast.role_filter IS NULL
               OR al.role = recall_fast.role_filter)
          AND (recall_fast.project_id_filter IS NULL
               OR al.project_id = recall_fast.project_id_filter)
          AND (recall_fast.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_fast.exclude_dag_id)
          -- P0.2: typed recall pushdown (ix_pgmnemo_content_type_active)
          AND (recall_fast.p_content_types IS NULL
               OR al.content_type = ANY(recall_fast.p_content_types))
        ORDER BY al.embedding <=> query_embedding
        LIMIT k
    ),
    stamped AS (
        UPDATE pgmnemo.agent_lesson al2
        SET
            last_recalled_at = NOW(),
            recall_count     = al2.recall_count + 1
        FROM fast_ranked fr
        WHERE al2.id = fr.id
          AND _track_recency
        RETURNING al2.id
    )
    SELECT
        fr.id          AS lesson_id,
        fr.vec_score   AS score,
        fr.role,
        fr.project_id,
        fr.topic,
        fr.lesson_text,
        fr.importance,
        fr.metadata,
        fr.commit_sha,
        fr.artifact_hash,
        fr.verified_at,
        fr.created_at
    FROM fast_ranked fr
    -- stamped CTE is a side-effect sink; reference it to prevent optimiser elision
    LEFT JOIN stamped s ON s.id = fr.id
    ORDER BY fr.vec_score DESC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_fast(vector, INT, TEXT, INT, TEXT, TEXT[]) IS
    'HNSW-only vector recall — O(k log n), no BM25/graph/RRF. '
    'Uses ORDER BY embedding <=> query LIMIT k to activate the HNSW index. '
    'score = cosine similarity (1 - distance). '
    'Respects include_unverified, ef_search, track_recall_recency GUCs. '
    'Filters: role_filter, project_id_filter, exclude_dag_id (same as recall_hybrid). '
    'v0.10.0: default MCP recall path. Use recall_hybrid for full 6-signal fusion. '
    'v0.10.1 #84: raises EXCEPTION when query_embedding IS NULL (no text-only fallback). '
    'v0.11.1: p_content_types TEXT[] DEFAULT NULL (6th param) — typed recall pushdown '
    'using ix_pgmnemo_content_type_active. NULL=all types (backward-compatible). '
    'Non-NULL restricts candidates to the given content_type values before HNSW ranking.';


-- ─────────────────────────────────────────────────────────────────────────────
-- 2. recall_lessons() — 7 → 8 params: add p_content_types TEXT[] DEFAULT NULL
--
-- Drop the old 7-param overload to prevent ambiguous-function errors.
-- The 8-param version is backward-compatible: existing 7-arg call sites pick
-- up DEFAULT NULL, and 6-arg calls (recall_lessons_pooled) remain unchanged.
--
-- Hybrid path:   p_content_types forwarded to recall_hybrid() as 10th param.
-- Vector-only path: WHERE pushdown on content_type = ANY(p_content_types).
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ, TEXT);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT         DEFAULT 10,
    role_filter       TEXT        DEFAULT NULL,
    project_id_filter INT         DEFAULT NULL,
    query_text        TEXT        DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ DEFAULT NULL,
    exclude_dag_id    TEXT        DEFAULT NULL,
    p_content_types   TEXT[]      DEFAULT NULL   -- P0.2: typed recall; NULL=all types
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
    _ghost_count        INT;
BEGIN
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT, 2000);
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars. Original: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    _has_vec  := query_embedding IS NOT NULL;
    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _disable_hybrid := FALSE;
    END;

    -- Hybrid path delegates to recall_hybrid (which handles stamping + exclude_dag_id)
    -- P0.2: p_content_types forwarded as the 10th argument of recall_hybrid().
    IF NOT _disable_hybrid AND _has_vec AND _has_text THEN
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        RETURN QUERY
        SELECT
            h.lesson_id, h.score, h.role, h.project_id, h.topic, h.lesson_text,
            h.importance, h.metadata, h.commit_sha, h.artifact_hash,
            h.verified_at, h.created_at,
            h.vec_score, h.bm25_score, h.rrf_score,
            h.confidence, h.match_confidence
        FROM pgmnemo.recall_hybrid(
            query_embedding, _query_text, k,
            role_filter, project_id_filter, 0.4, 0.4, 60,
            exclude_dag_id, p_content_types   -- P0.2: pass typed recall filter
        ) h;
        RETURN;
    END IF;

    -- Vector-only path (pgmnemo.disable_hybrid = 'true' or no query_text)
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05);
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION, 1.0)));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('simple', left(trim(_query_text), 200));  -- Fix 4+1
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', left(trim(_query_text), 200));   -- Fix 4+1
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at,
            al.confidence,
            CASE
                WHEN al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery     -- Fix 2: indexed full_text
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL
               OR al.project_id = recall_lessons.project_id_filter)
          AND (recall_lessons.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_lessons.exclude_dag_id)
          AND (al.embedding IS NOT NULL OR _has_text)
          -- P0.2: typed recall pushdown (ix_pgmnemo_content_type_active)
          AND (recall_lessons.p_content_types IS NULL
               OR al.content_type = ANY(recall_lessons.p_content_types))
    ),
    anchors AS (
        SELECT id FROM candidates ORDER BY vec_score DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    scored AS (
        SELECT
            c.id, c.role, c.project_id, c.topic, c.lesson_text, c.importance,
            c.metadata, c.commit_sha, c.artifact_hash, c.verified_at, c.created_at,
            c.confidence,
            c.vec_score,
            c.ft_score,
            (c.vec_score + _gamma * GREATEST(0.0, 1.0 - LEAST(
                 EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0), 1.0
             ))) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
             + c.ft_score * 0.1
             AS combined_score
        FROM candidates c
        LEFT JOIN graph_proximity gp ON gp.lesson_id = c.id
    )
    SELECT
        s.id                  AS lesson_id,
        s.combined_score      AS score,
        s.role,
        s.project_id,
        s.topic,
        s.lesson_text,
        s.importance,
        s.metadata,
        s.commit_sha,
        s.artifact_hash,
        s.verified_at,
        s.created_at,
        s.vec_score,
        s.ft_score            AS bm25_score,
        0.0::DOUBLE PRECISION AS rrf_score,
        s.confidence::REAL,
        LEAST(1.0, GREATEST(0.0, s.vec_score))::REAL AS match_confidence
    FROM scored s
    ORDER BY s.combined_score DESC, s.id ASC
    LIMIT k;

    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active AND al.t_valid_to = 'infinity'::TIMESTAMPTZ AND al.verified_at IS NULL
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL
               OR al.project_id = recall_lessons.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % unverified lesson(s) excluded. '
                'SET pgmnemo.include_unverified = ''on'' to include them.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ, TEXT, TEXT[]) IS
    'v0.11.1 hybrid router with diagnostic columns and typed recall. '
    'Routes to recall_hybrid() when both query_embedding and query_text are present '
    '(and pgmnemo.disable_hybrid is FALSE/unset). '
    'Falls back to vector-only (HNSW + recency + graph) when query_text is absent. '
    'p_content_types TEXT[] DEFAULT NULL (8th param, v0.11.1): typed recall pushdown. '
    'On the hybrid path: forwarded to recall_hybrid() as the 10th argument. '
    'On the vector-only path: applied as WHERE content_type = ANY(p_content_types). '
    'NULL=all types (backward-compatible). Non-NULL filters by content type before ranking. '
    'GIN-indexed for BM25 retrieval via ts_rank_cd in recall_hybrid(). '
    'Respects pgmnemo.disable_hybrid, ef_search, include_unverified, recency_weight, '
    'temporal_boost, graph_proximity_weight, max_query_text_chars GUCs.';

-- ─────────────────────────────────────────────────────────────────────────────
-- Version bump notice
-- ─────────────────────────────────────────────────────────────────────────────

DO $$ BEGIN
    RAISE NOTICE 'pgmnemo version "0.11.1" installed successfully.';
END $$;

-- ─────────────────────────────────────────────────────────────────────────
-- pgmnemo 0.12.0 additions (Typed Write API: remember_fact/event/relation)
-- ─────────────────────────────────────────────────────────────────────────
-- pgmnemo--0.11.1--0.12.0.sql
-- pgmnemo upgrade 0.11.1 → 0.12.0
-- v0.12.0: Typed Write API — remember_fact / remember_event / remember_relation
-- RFC-001 §D2 + ADDENDUM-2 (7 correctness requirements)
--
-- No breaking changes. No schema column additions. All new symbols.
-- SPDX-License-Identifier: Apache-2.0


DO $$ BEGIN RAISE NOTICE 'pgmnemo: upgrading to version 0.12.0 (Typed Write API)'; END; $$;

CREATE UNIQUE INDEX IF NOT EXISTS ix_entity_canonical_name_prj
    ON pgmnemo.agent_lesson (
        lower(metadata->>'canonical_name'),
        COALESCE(project_id, -1)
    )
    WHERE content_type = 'entity'
      AND is_active
      AND t_valid_to = 'infinity'::TIMESTAMPTZ;

COMMENT ON INDEX pgmnemo.ix_entity_canonical_name_prj IS
    'Unique entity hub per (lower(canonical_name), project_id). v0.12.0.';

-- guard_no_test_project
CREATE OR REPLACE FUNCTION pgmnemo.guard_no_test_project(
    p_project_id INT,
    p_allowed_db TEXT DEFAULT NULL
) RETURNS VOID LANGUAGE plpgsql AS $$
DECLARE
    v_floor INT;
BEGIN
    IF p_project_id IS NULL THEN
        RAISE EXCEPTION 'pgmnemo.guard_no_test_project: p_project_id IS NULL — tests must use an explicit test project_id';
    END IF;
    v_floor := COALESCE(NULLIF(current_setting('pgmnemo.test_project_floor', true), '')::int, 0);
    IF p_project_id <= v_floor THEN
        RAISE EXCEPTION 'pgmnemo.guard_no_test_project: project_id=% is at/below the configured production floor (pgmnemo.test_project_floor=%). Use a higher test project_id.', p_project_id, v_floor;
    END IF;
    IF p_allowed_db IS NOT NULL AND current_database() <> p_allowed_db THEN
        RAISE EXCEPTION 'pgmnemo.guard_no_test_project: must run on ''%'', current db is ''%''.', p_allowed_db, current_database();
    END IF;
END;
$$;
COMMENT ON FUNCTION pgmnemo.guard_no_test_project(INT, TEXT) IS 'Safety guard for test harnesses. Raises when p_project_id IS NULL or <= GUC pgmnemo.test_project_floor (default 0 = floor check disabled; callers opt in). RFC-001 testing guidance. v0.12.1.';

-- _evict_prior_lesson: shared bitemporal eviction helper (RFC-001 §D3 / ADDENDUM-2 R3)
-- Called by remember_fact and remember_relation to close a prior active row.
-- Centralises the t_valid_to / state / is_active / updated_at update so that
-- all supersession paths are byte-for-byte identical and a single fix propagates everywhere.
CREATE OR REPLACE FUNCTION pgmnemo._evict_prior_lesson(p_lesson_id BIGINT)
RETURNS VOID LANGUAGE sql AS $$
    UPDATE pgmnemo.agent_lesson
    SET t_valid_to = NOW(),
        state      = 'superseded',
        is_active  = FALSE,
        updated_at = NOW()
    WHERE id = p_lesson_id;
$$;
COMMENT ON FUNCTION pgmnemo._evict_prior_lesson(BIGINT) IS
    'Bitemporal eviction: close active lesson (t_valid_to=NOW, state=superseded, is_active=FALSE). '
    'Shared helper called by remember_fact + remember_relation supersession paths. '
    'RFC-001 §D3 + ADDENDUM-2 R3. v0.12.0.';

-- _has_contact_pii
CREATE OR REPLACE FUNCTION pgmnemo._has_contact_pii(p_property TEXT)
RETURNS BOOLEAN LANGUAGE sql IMMUTABLE STRICT PARALLEL SAFE AS $$
    SELECT p_property IN ('email', 'phone', 'address', 'telegram', 'full_name');
$$;
COMMENT ON FUNCTION pgmnemo._has_contact_pii(TEXT) IS 'PII property detector. Returns TRUE for {email,phone,address,telegram,full_name}. PROPERTY_CONVENTIONS §5.1 / RFC-001 §D4. v0.12.0.';

-- canonical_slug
CREATE OR REPLACE FUNCTION pgmnemo.canonical_slug(p_type TEXT, p_label TEXT)
RETURNS TEXT LANGUAGE plpgsql IMMUTABLE PARALLEL SAFE AS $$
DECLARE
    _id    TEXT;
    _slug  TEXT;
    _valid CONSTANT TEXT[] := ARRAY['person','org','project','product','location','concept'];
BEGIN
    IF p_type IS NULL OR NOT (p_type = ANY(_valid)) THEN
        RAISE EXCEPTION 'pgmnemo.canonical_slug: unknown type prefix ''%'' — must be one of: person, org, project, product, location, concept', p_type;
    END IF;
    IF p_label IS NULL OR length(trim(p_label)) = 0 THEN
        RAISE EXCEPTION 'pgmnemo.canonical_slug: p_label must not be NULL or empty';
    END IF;
    _id := lower(p_label);
    _id := regexp_replace(_id, '[^a-z0-9]+', '_', 'g');
    _id := trim(BOTH '_' FROM _id);
    IF length(_id) > 64 THEN
        _id := substring(_id FOR 64);
        IF _id ~ '_' THEN _id := regexp_replace(_id, '_[^_]*$', ''); END IF;
        _id := trim(BOTH '_' FROM _id);
    END IF;
    _slug := p_type || ':' || _id;
    IF _id = '' OR _slug !~ '^(person|org|project|product|location|concept):[a-z0-9_]+$' THEN
        RAISE EXCEPTION 'pgmnemo.canonical_slug: could not normalise ''%'' (result: ''%'')', p_label, _slug;
    END IF;
    IF length(_slug) > 72 THEN
        RAISE EXCEPTION 'pgmnemo.canonical_slug: slug ''%'' exceeds 72 chars', _slug;
    END IF;
    RETURN _slug;
END;
$$;
COMMENT ON FUNCTION pgmnemo.canonical_slug(TEXT, TEXT) IS 'Normalise label into canonical slug ^(person|org|project|product|location|concept):[a-z0-9_]+$ <=72. SLUG_CONVENTION §4. v0.12.0.';

-- remember_fact
CREATE OR REPLACE FUNCTION pgmnemo.remember_fact(
    p_role            TEXT,
    p_entity_key      TEXT,
    p_property        TEXT,
    p_value           TEXT,
    p_confidence      REAL        DEFAULT 0.7,
    p_has_contact_pii BOOLEAN     DEFAULT NULL,
    p_embedding       vector(1024) DEFAULT NULL,
    p_source_type     TEXT        DEFAULT NULL,
    p_project_id      INT         DEFAULT NULL,
    p_commit_sha      TEXT        DEFAULT NULL,
    p_artifact_hash   TEXT        DEFAULT NULL
) RETURNS TABLE(id BIGINT, final_state TEXT)
LANGUAGE plpgsql AS $$
DECLARE
    _topic         TEXT;
    _artifact_hash TEXT;
    _is_pii        BOOLEAN;
    _final_state   TEXT;
    _merge_state   TEXT;    -- actual post-merge state (may differ from _final_state for promote)
    _prior         pgmnemo.agent_lesson%ROWTYPE;
    _new_id        BIGINT;
    _version_n     INT;
    _eff_source    TEXT;
BEGIN
    -- Input guards
    IF p_entity_key IS NULL THEN
        RAISE EXCEPTION 'pgmnemo.remember_fact: p_entity_key must not be NULL';
    END IF;
    IF p_entity_key !~ '^(person|org|project|product|location|concept):[a-z0-9_]+$' THEN
        RAISE EXCEPTION 'pgmnemo.remember_fact: invalid entity_key ''%'' — must match slug regex', p_entity_key;
    END IF;
    IF p_property IS NULL OR length(trim(p_property)) = 0 THEN
        RAISE EXCEPTION 'pgmnemo.remember_fact: p_property must not be NULL or empty';
    END IF;
    IF p_value IS NULL THEN
        RAISE EXCEPTION 'pgmnemo.remember_fact: p_value must not be NULL';
    END IF;
    IF p_confidence IS NOT NULL AND (p_confidence < 0.0 OR p_confidence > 1.0) THEN
        RAISE EXCEPTION 'pgmnemo.remember_fact: p_confidence % out of [0,1]', p_confidence;
    END IF;
    IF p_source_type IS NOT NULL AND
       p_source_type NOT IN ('system','agent_authored','auto_captured','imported') THEN
        RAISE EXCEPTION 'pgmnemo.remember_fact: invalid source_type ''%''', p_source_type;
    END IF;

    -- R2: Synthesize artifact_hash (COALESCE BEFORE gate can inspect)
    -- topic: lower(entity_key)/lower(property) per RFC-001 §D2
    _topic         := lower(p_entity_key) || '/' || lower(p_property);
    _artifact_hash := COALESCE(p_artifact_hash, 'fact-' || p_entity_key || ':' || p_property);

    -- R1/R7: PII detection — explicit override wins, else auto-detect
    _is_pii := COALESCE(
        p_has_contact_pii,
        pgmnemo._has_contact_pii(p_property) AND (p_entity_key LIKE 'person:%')
    );

    -- R1: State routing (RFC-001 §D4)
    -- PII on person:* → candidate ALWAYS (even system source)
    IF _is_pii THEN
        _final_state := 'candidate';
    ELSIF p_source_type = 'system' THEN
        _final_state := 'validated';
    ELSIF p_source_type = 'auto_captured' THEN
        _final_state := 'candidate';
    ELSIF p_source_type = 'agent_authored' AND COALESCE(p_confidence, 0.0) >= 0.8 THEN
        _final_state := 'validated';
    ELSE
        -- agent_authored low-conf, imported, NULL source_type → candidate
        _final_state := 'candidate';
    END IF;

    _eff_source := COALESCE(p_source_type, 'agent_authored');

    -- R3: Identity/dedup on (lower(topic), project_id) with FOR UPDATE
    SELECT * INTO _prior
    FROM pgmnemo.agent_lesson
    WHERE lower(topic) = lower(_topic)
      AND (p_project_id IS NULL OR project_id = p_project_id)
      AND is_active
      AND t_valid_to = 'infinity'::TIMESTAMPTZ
    ORDER BY version_n DESC
    LIMIT 1
    FOR UPDATE;

    IF FOUND THEN
        IF lower(trim(_prior.lesson_text)) = lower(trim(p_value)) THEN
            -- MERGE: same value — update confidence + promote state, no new version.
            -- Promotion rule: 'validated' wins over 'candidate'/'draft'; never demote.
            UPDATE pgmnemo.agent_lesson
            SET confidence = GREATEST(confidence, COALESCE(p_confidence, 0.7)),
                state      = CASE
                                 WHEN _final_state = 'validated'
                                      AND state NOT IN ('validated', 'canonical')
                                 THEN 'validated'
                                 ELSE state
                             END
            WHERE agent_lesson.id = _prior.id
            RETURNING agent_lesson.state INTO _merge_state;
            RETURN QUERY SELECT _prior.id, _merge_state;
            RETURN;
        ELSE
            -- SUPERSEDE: different value — close prior row via shared eviction helper, open new version
            PERFORM pgmnemo._evict_prior_lesson(_prior.id);
            _version_n := COALESCE(_prior.version_n, 0) + 1;
        END IF;
    ELSE
        _version_n := 1;
    END IF;

    -- INSERT new fact row
    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance,
        embedding, commit_sha, artifact_hash, metadata,
        source_type, content_type, state, confidence,
        version_n, verified_at, t_valid_from, t_valid_to
    ) VALUES (
        p_role, p_project_id, _topic, p_value, 3,
        p_embedding, p_commit_sha, _artifact_hash,
        jsonb_build_object(
            'canonical_name', p_entity_key,
            'entity_key',     p_entity_key,
            'property',       p_property
        ),
        _eff_source, 'fact', _final_state,
        COALESCE(p_confidence, 0.7),
        _version_n,
        -- validated → visible to recall; candidate → ghost (verified_at NULL)
        CASE WHEN _final_state = 'validated' THEN NOW() ELSE NULL END,
        NOW(),
        'infinity'::TIMESTAMPTZ
    )
    RETURNING agent_lesson.id INTO _new_id;

    RETURN QUERY SELECT _new_id, _final_state;
END;
$$;
COMMENT ON FUNCTION pgmnemo.remember_fact(TEXT,TEXT,TEXT,TEXT,REAL,BOOLEAN,vector,TEXT,INT,TEXT,TEXT) IS
    'Typed fact write with bitemporal supersession, PII-aware state routing, synthesized artifact_hash. RFC-001 §D2 + ADDENDUM-2 R1-R7. v0.12.0.';

-- remember_event
CREATE OR REPLACE FUNCTION pgmnemo.remember_event(
    p_role          TEXT,
    p_entity_key    TEXT,
    p_event_label   TEXT,
    p_event_body    TEXT,
    p_occurred_at   TIMESTAMPTZ  DEFAULT NOW(),
    p_confidence    REAL         DEFAULT 0.8,
    p_embedding     vector(1024) DEFAULT NULL,
    p_source_type   TEXT         DEFAULT NULL,
    p_project_id    INT          DEFAULT NULL,
    p_commit_sha    TEXT         DEFAULT NULL,
    p_artifact_hash TEXT         DEFAULT NULL
) RETURNS BIGINT LANGUAGE plpgsql AS $$
DECLARE
    _topic         TEXT;
    _artifact_hash TEXT;
    _final_state   TEXT;
    _new_id        BIGINT;
BEGIN
    IF p_entity_key IS NULL OR p_entity_key !~ '^(person|org|project|product|location|concept):[a-z0-9_]+$' THEN
        RAISE EXCEPTION 'pgmnemo.remember_event: invalid entity_key ''%''', p_entity_key;
    END IF;
    IF p_event_label IS NULL OR length(trim(p_event_label)) = 0 THEN
        RAISE EXCEPTION 'pgmnemo.remember_event: p_event_label must not be NULL or empty';
    END IF;
    IF p_event_body IS NULL OR length(trim(p_event_body)) < 1 THEN
        RAISE EXCEPTION 'pgmnemo.remember_event: p_event_body must not be NULL or empty';
    END IF;
    _topic         := p_entity_key || ':event:' || p_event_label;
    _artifact_hash := COALESCE(p_artifact_hash, 'event-' || p_entity_key || ':' || p_event_label);

    -- R3: Idempotency — FOR UPDATE dedup on (lower(topic), project_id).
    -- Events are append-only (no supersession); same (entity_key, event_label, project_id)
    -- returns the existing row id. Prevents duplicate event rows across concurrent writes.
    SELECT id INTO _new_id
    FROM pgmnemo.agent_lesson
    WHERE lower(topic) = lower(_topic)
      AND (p_project_id IS NULL OR project_id = p_project_id)
      AND is_active
      AND t_valid_to = 'infinity'::TIMESTAMPTZ
    LIMIT 1 FOR UPDATE;
    IF FOUND THEN
        RETURN _new_id;
    END IF;

    IF p_source_type = 'auto_captured' THEN
        _final_state := 'candidate';
    ELSIF p_source_type = 'system' THEN
        _final_state := 'validated';
    ELSIF p_source_type = 'agent_authored' AND COALESCE(p_confidence, 0.8) >= 0.8 THEN
        _final_state := 'validated';
    ELSE
        _final_state := 'candidate';
    END IF;
    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance,
        embedding, commit_sha, artifact_hash, metadata,
        source_type, content_type, state, confidence,
        version_n, verified_at, t_valid_from, t_valid_to
    ) VALUES (
        p_role, p_project_id, _topic, p_event_body, 3,
        p_embedding, p_commit_sha, _artifact_hash,
        jsonb_build_object('entity_key', p_entity_key, 'event_label', p_event_label, 'occurred_at', p_occurred_at),
        COALESCE(p_source_type, 'agent_authored'), 'event', _final_state,
        COALESCE(p_confidence, 0.8), 1,
        CASE WHEN _final_state = 'validated' THEN NOW() ELSE NULL END,
        COALESCE(p_occurred_at, NOW()), 'infinity'::TIMESTAMPTZ
    ) RETURNING agent_lesson.id INTO _new_id;
    RETURN _new_id;
END;
$$;
COMMENT ON FUNCTION pgmnemo.remember_event(TEXT,TEXT,TEXT,TEXT,TIMESTAMPTZ,REAL,vector,TEXT,INT,TEXT,TEXT) IS 'Immutable event record. Append-only. content_type=event. RFC-001 §D2. v0.12.0.';

-- remember_relation
CREATE OR REPLACE FUNCTION pgmnemo.remember_relation(
    p_role          TEXT,
    p_from_key      TEXT,
    p_to_key        TEXT,
    p_relation_type TEXT,
    p_confidence    REAL         DEFAULT 0.7,
    p_embedding     vector(1024) DEFAULT NULL,
    p_source_type   TEXT         DEFAULT NULL,
    p_project_id    INT          DEFAULT NULL,
    p_commit_sha    TEXT         DEFAULT NULL,
    p_artifact_hash TEXT         DEFAULT NULL
) RETURNS BIGINT LANGUAGE plpgsql AS $$
DECLARE
    _topic         TEXT;
    _artifact_hash TEXT;
    _final_state   TEXT;
    _prior_id      BIGINT;
    _new_id        BIGINT;
    _from_hub_id   BIGINT;
    _to_hub_id     BIGINT;
BEGIN
    IF p_from_key IS NULL OR p_from_key !~ '^(person|org|project|product|location|concept):[a-z0-9_]+$' THEN
        RAISE EXCEPTION 'pgmnemo.remember_relation: invalid from_key ''%''', p_from_key;
    END IF;
    IF p_to_key IS NULL OR p_to_key !~ '^(person|org|project|product|location|concept):[a-z0-9_]+$' THEN
        RAISE EXCEPTION 'pgmnemo.remember_relation: invalid to_key ''%''', p_to_key;
    END IF;
    IF p_relation_type IS NULL OR length(trim(p_relation_type)) = 0 THEN
        RAISE EXCEPTION 'pgmnemo.remember_relation: p_relation_type must not be NULL or empty';
    END IF;
    _topic         := p_from_key || ':' || p_relation_type || ':' || p_to_key;
    _artifact_hash := COALESCE(p_artifact_hash, 'rel-' || p_from_key || ':' || p_relation_type || ':' || p_to_key);
    IF p_source_type = 'system' THEN _final_state := 'validated';
    ELSIF p_source_type = 'auto_captured' THEN _final_state := 'candidate';
    ELSIF p_source_type = 'agent_authored' AND COALESCE(p_confidence, 0.7) >= 0.8 THEN _final_state := 'validated';
    ELSE _final_state := 'candidate';
    END IF;
    SELECT id INTO _prior_id FROM pgmnemo.agent_lesson
    WHERE lower(topic) = lower(_topic)
      AND (p_project_id IS NULL OR project_id = p_project_id)
      AND is_active AND t_valid_to = 'infinity'::TIMESTAMPTZ
    LIMIT 1 FOR UPDATE;
    IF FOUND THEN
        UPDATE pgmnemo.agent_lesson SET confidence = GREATEST(confidence, COALESCE(p_confidence, 0.7)), updated_at = NOW() WHERE agent_lesson.id = _prior_id;
        RETURN _prior_id;
    END IF;
    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance,
        embedding, commit_sha, artifact_hash, metadata,
        source_type, content_type, state, confidence,
        version_n, verified_at, t_valid_from, t_valid_to
    ) VALUES (
        p_role, p_project_id, _topic,
        p_from_key || ' ' || p_relation_type || ' ' || p_to_key, 3,
        p_embedding, p_commit_sha, _artifact_hash,
        jsonb_build_object('from_key', p_from_key, 'to_key', p_to_key, 'relation_type', p_relation_type),
        COALESCE(p_source_type, 'agent_authored'), 'relation', _final_state,
        COALESCE(p_confidence, 0.7), 1,
        CASE WHEN _final_state = 'validated' THEN NOW() ELSE NULL END,
        NOW(), 'infinity'::TIMESTAMPTZ
    ) RETURNING agent_lesson.id INTO _new_id;
    BEGIN
        SELECT id INTO _from_hub_id FROM pgmnemo.agent_lesson WHERE content_type = 'entity' AND lower(topic) = lower(p_from_key) AND (p_project_id IS NULL OR project_id = p_project_id) AND is_active AND t_valid_to = 'infinity'::TIMESTAMPTZ LIMIT 1;
        SELECT id INTO _to_hub_id FROM pgmnemo.agent_lesson WHERE content_type = 'entity' AND lower(topic) = lower(p_to_key) AND (p_project_id IS NULL OR project_id = p_project_id) AND is_active AND t_valid_to = 'infinity'::TIMESTAMPTZ LIMIT 1;
        IF _from_hub_id IS NOT NULL AND _to_hub_id IS NOT NULL THEN
            PERFORM pgmnemo.add_edge(_from_hub_id, _to_hub_id, p_relation_type, COALESCE(p_confidence, 0.7)::FLOAT8, jsonb_build_object('relation_lesson_id', _new_id), 'max');
        ELSE
            RAISE NOTICE 'pgmnemo.remember_relation: entity hubs not found for ''%'' or ''%'' — mem_edge skipped; lesson_id=% recorded.', p_from_key, p_to_key, _new_id;
        END IF;
    EXCEPTION WHEN OTHERS THEN
        RAISE NOTICE 'pgmnemo.remember_relation: add_edge failed (lesson_id=%): %', _new_id, SQLERRM;
    END;
    RETURN _new_id;
END;
$$;
COMMENT ON FUNCTION pgmnemo.remember_relation(TEXT,TEXT,TEXT,TEXT,REAL,vector,TEXT,INT,TEXT,TEXT) IS 'Directed typed relation. Idempotent on triple. content_type=relation. RFC-001 §D2. v0.12.0.';


-- ═══════════════════════════════════════════════════════════════════════════
-- pgmnemo 0.12.2 → 0.13.0 delta applied inline
-- ═══════════════════════════════════════════════════════════════════════════

-- pgmnemo--0.12.2--0.13.0.sql
-- Upgrade: 0.12.2 → 0.13.0 — Outcome Loop v2 (posterior confidence + use-attribution + min_score)
-- SPDX-License-Identifier: Apache-2.0

-- §1: Add use_count column (tracks confirmed attribution separate from recall_count)
ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS use_count INT NOT NULL DEFAULT 0;

COMMENT ON COLUMN pgmnemo.agent_lesson.use_count IS
    'Number of times this lesson was confirmed used (p_used=TRUE or NULL). '
    'Separate from recall_count (shown) and success_count+fail_count (outcome). '
    'Added in v0.13.0.';

-- §2a: Backfill use_count from existing observations
UPDATE pgmnemo.agent_lesson
SET use_count = success_count + fail_count
WHERE success_count + fail_count > 0;

-- §2b: Recompute confidence via Beta(1,1) posterior for lessons with observations
UPDATE pgmnemo.agent_lesson
SET confidence = LEAST(1.0, GREATEST(0.0,
    (success_count + 1.0) / (success_count + fail_count + 2.0)
))::REAL
WHERE success_count + fail_count > 0;

-- §2c: Reset confidence to 0.5 for zero-observation lessons (prior mean with Beta(1,1))
UPDATE pgmnemo.agent_lesson
SET confidence = 0.5
WHERE success_count = 0 AND fail_count = 0
  AND confidence != 0.5;

-- §3: New 3-param scalar reinforce (v0.13.0 — posterior confidence + use-attribution)
-- NOTE: p_used has NO DEFAULT — use 2-param shim below for backward compat (p_used=NULL).
-- No default avoids ambiguity with reinforce(BIGINT, TEXT) during overload resolution.
CREATE OR REPLACE FUNCTION pgmnemo.reinforce(
    p_lesson_id  BIGINT,
    p_outcome    TEXT,
    p_used       BOOLEAN
)
RETURNS REAL
LANGUAGE plpgsql
AS $func$
#variable_conflict use_column
DECLARE
    _row            pgmnemo.agent_lesson%ROWTYPE;
    _new_conf       REAL;
    _mode           TEXT;
    _alpha          DOUBLE PRECISION;
    _beta           DOUBLE PRECISION;
    _success_delta  DOUBLE PRECISION;
    _fail_delta     DOUBLE PRECISION;
    _effective_used BOOLEAN;
BEGIN
    -- Resolve effective p_used: NULL → TRUE (backward compat)
    _effective_used := COALESCE(p_used, TRUE);

    -- Read confidence mode
    -- current_setting(..., TRUE) returns NULL when unset and never raises,
    -- so no exception wrapper: an unknown mode MUST surface, not silently
    -- fall back (a swallowed RAISE here defeats the validation entirely).
    _mode := COALESCE(
        NULLIF(current_setting('pgmnemo.confidence_mode', TRUE), ''),
        'posterior');
    IF _mode NOT IN ('posterior', 'additive') THEN
        RAISE EXCEPTION
            'pgmnemo.reinforce: unknown confidence_mode ''%'' — expected ''posterior'' or ''additive''',
            _mode;
    END IF;

    -- Lock row
    SELECT * INTO _row
    FROM pgmnemo.agent_lesson
    WHERE id = p_lesson_id
    FOR UPDATE;

    IF NOT FOUND THEN
        RAISE EXCEPTION 'pgmnemo.reinforce: lesson_id % not found', p_lesson_id;
    END IF;

    -- If lesson was not used, skip count update — preserve confidence
    IF NOT _effective_used THEN
        RETURN _row.confidence;
    END IF;

    -- Update outcome counters (always, regardless of mode)
    CASE p_outcome
        WHEN 'success' THEN
            UPDATE pgmnemo.agent_lesson
            SET success_count   = _row.success_count + 1,
                use_count       = _row.use_count + 1,
                last_outcome    = 'success',
                last_outcome_at = NOW()
            WHERE id = p_lesson_id;

        WHEN 'failure' THEN
            UPDATE pgmnemo.agent_lesson
            SET fail_count      = _row.fail_count + 1,
                use_count       = _row.use_count + 1,
                last_outcome    = 'failure',
                last_outcome_at = NOW()
            WHERE id = p_lesson_id;

        WHEN 'neutral' THEN
            -- Neutral: increment use_count but not success/fail
            UPDATE pgmnemo.agent_lesson
            SET use_count       = _row.use_count + 1,
                last_outcome    = 'neutral',
                last_outcome_at = NOW()
            WHERE id = p_lesson_id;
            RETURN _row.confidence;  -- no confidence change for neutral

        ELSE
            RAISE EXCEPTION
                'pgmnemo.reinforce: unknown outcome ''%'' — expected ''success'', ''failure'', or ''neutral''',
                p_outcome;
    END CASE;

    -- Compute new confidence
    IF _mode = 'posterior' THEN
        -- Read prior hyperparameters
        BEGIN
            _alpha := GREATEST(0.01, LEAST(100.0, COALESCE(
                NULLIF(current_setting('pgmnemo.confidence_prior_alpha', TRUE), '')::DOUBLE PRECISION,
                1.0)));
        EXCEPTION WHEN OTHERS THEN _alpha := 1.0;
        END;

        BEGIN
            _beta := GREATEST(0.01, LEAST(100.0, COALESCE(
                NULLIF(current_setting('pgmnemo.confidence_prior_beta', TRUE), '')::DOUBLE PRECISION,
                1.0)));
        EXCEPTION WHEN OTHERS THEN _beta := 1.0;
        END;

        -- Read updated counts (after the UPDATE above)
        SELECT success_count, fail_count INTO _row.success_count, _row.fail_count
        FROM pgmnemo.agent_lesson WHERE id = p_lesson_id;

        _new_conf := ((_row.success_count + _alpha) /
                      (_row.success_count + _row.fail_count + _alpha + _beta))::REAL;

    ELSE  -- 'additive' (legacy, deprecated)
        BEGIN
            _success_delta := GREATEST(0.001, LEAST(0.5, COALESCE(
                NULLIF(current_setting('pgmnemo.reinforce_success_delta', TRUE), '')::DOUBLE PRECISION,
                0.02)));
        EXCEPTION WHEN OTHERS THEN _success_delta := 0.02;
        END;

        BEGIN
            _fail_delta := GREATEST(0.001, LEAST(0.5, COALESCE(
                NULLIF(current_setting('pgmnemo.reinforce_fail_delta', TRUE), '')::DOUBLE PRECISION,
                0.12)));
        EXCEPTION WHEN OTHERS THEN _fail_delta := 0.12;
        END;

        IF p_outcome = 'success' THEN
            _new_conf := LEAST(1.0, _row.confidence + _success_delta::REAL);
        ELSE  -- failure (neutral already returned above)
            _new_conf := GREATEST(0.0, _row.confidence - _fail_delta::REAL);
        END IF;
    END IF;

    -- Clamp and persist
    _new_conf := LEAST(1.0, GREATEST(0.0, _new_conf));

    UPDATE pgmnemo.agent_lesson
    SET confidence = _new_conf
    WHERE id = p_lesson_id;

    RETURN _new_conf;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reinforce(BIGINT, TEXT, BOOLEAN) IS
    'v0.13.0 Outcome Loop v2. '
    'p_outcome: ''success'' | ''failure'' | ''neutral'' (exact case). '
    'p_used: NULL/TRUE = lesson was used (counts updated, confidence recomputed); '
    '        FALSE = lesson shown but not used (no count/confidence change). '
    'Mode pgmnemo.confidence_mode: ''posterior'' (default, Beta posterior mean) '
    'or ''additive'' (legacy delta scheme, deprecated). '
    'Prior: pgmnemo.confidence_prior_alpha/beta (default 1.0/1.0 = uniform).';

-- §4: New 3-param batch reinforce (delegates to scalar form)
-- NOTE: p_used has NO DEFAULT — use 2-param shim below for backward compat.
CREATE OR REPLACE FUNCTION pgmnemo.reinforce(
    p_lesson_ids  BIGINT[],
    p_outcome     TEXT,
    p_used        BOOLEAN
)
RETURNS INT
LANGUAGE plpgsql
AS $func$
DECLARE
    _id              BIGINT;
    _count           INT := 0;
    _effective_used  BOOLEAN;
BEGIN
    IF p_lesson_ids IS NULL OR array_length(p_lesson_ids, 1) IS NULL THEN
        RETURN 0;
    END IF;

    IF p_outcome NOT IN ('success', 'failure', 'neutral') THEN
        RAISE EXCEPTION
            'pgmnemo.reinforce: unknown outcome ''%'' — expected ''success'', ''failure'', or ''neutral''',
            p_outcome;
    END IF;

    _effective_used := COALESCE(p_used, TRUE);

    FOREACH _id IN ARRAY p_lesson_ids LOOP
        BEGIN
            PERFORM pgmnemo.reinforce(_id, p_outcome, _effective_used);
            -- v0.7.1 contract: return value counts confidence-updated lessons.
            -- Neutral outcomes are stamped (last_outcome, use attribution) but
            -- do not change confidence, so they are not counted.
            IF p_outcome <> 'neutral' THEN
                _count := _count + 1;
            END IF;
        EXCEPTION WHEN OTHERS THEN
            -- Skip missing lesson_ids silently (batch contract)
            NULL;
        END;
    END LOOP;

    RETURN _count;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reinforce(BIGINT[], TEXT, BOOLEAN) IS
    'v0.13.0 batch reinforce. Delegates to scalar form per id. '
    'Skips missing/errored ids silently. Returns count of confidence-updated lessons; neutral outcomes are stamped for attribution but not counted (v0.7.1 contract).';

-- §5: Update 2-param scalar reinforce to delegate to 3-param (backward compat)
CREATE OR REPLACE FUNCTION pgmnemo.reinforce(
    p_lesson_id BIGINT,
    p_outcome   TEXT
)
RETURNS REAL
LANGUAGE plpgsql
AS $func$
BEGIN
    RETURN pgmnemo.reinforce(p_lesson_id, p_outcome, NULL::BOOLEAN);
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reinforce(BIGINT, TEXT) IS
    'v0.13.0 compat shim: delegates to reinforce(BIGINT, TEXT, BOOLEAN) with p_used=NULL. '
    'DEPRECATED: use 3-param form. Will be removed in v0.14.0.';

-- §5b: Update 2-param batch reinforce to delegate to 3-param
CREATE OR REPLACE FUNCTION pgmnemo.reinforce(
    p_lesson_ids BIGINT[],
    p_outcome    TEXT
)
RETURNS INT
LANGUAGE plpgsql
AS $func$
BEGIN
    RETURN pgmnemo.reinforce(p_lesson_ids, p_outcome, NULL::BOOLEAN);
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reinforce(BIGINT[], TEXT) IS
    'v0.13.0 compat shim: delegates to reinforce(BIGINT[], TEXT, BOOLEAN) with p_used=NULL. '
    'DEPRECATED: use 3-param form. Will be removed in v0.14.0.';

-- §6: recall_hybrid — add p_min_score (11th param)
--
-- Drop old 10-param overload to prevent ambiguous-function errors.
-- The 11-param version is backward-compatible: p_min_score DEFAULT NULL.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_hybrid(
    vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT, TEXT, text[]
);

-- ─────────────────────────────────────────────────────────────────────────────
-- recall_hybrid() v0.14.1 — HNSW planner regression fix
-- ─────────────────────────────────────────────────────────────────────────────
CREATE OR REPLACE FUNCTION pgmnemo.recall_hybrid(
    query_embedding   vector(1024),
    query_text        TEXT,
    k                 INT              DEFAULT 10,
    role_filter       TEXT             DEFAULT NULL,
    project_id_filter INT              DEFAULT NULL,
    vec_weight        DOUBLE PRECISION DEFAULT 0.4,
    bm25_weight       DOUBLE PRECISION DEFAULT 0.4,
    rrf_k             INT              DEFAULT 60,
    exclude_dag_id    TEXT             DEFAULT NULL,
    p_content_types   text[]           DEFAULT NULL,
    p_min_score       REAL             DEFAULT NULL
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _graph_weight       DOUBLE PRECISION;
    _max_depth          CONSTANT INT := 5;
    _rrf_k_f            DOUBLE PRECISION;
    _aux_scale          CONSTANT DOUBLE PRECISION := (0.8 / 61.0) / 0.76;
    _as_of_ts           TIMESTAMPTZ;
    _raw_blend_weight   DOUBLE PRECISION;
    _ghost_count        INT;
    _fetch_k_vec        INT;
    _fetch_k_bm25       INT;
    _conf_boost_w       DOUBLE PRECISION;
    -- 0.10.1 additions (#87)
    _lexical_text       TEXT;
    _bm25_budget_ms     INT;
    _bm25_timed_out     BOOLEAN := FALSE;
BEGIN
    _has_vec  := query_embedding IS NOT NULL;
    _has_text := query_text IS NOT NULL AND length(trim(query_text)) > 0;

    IF NOT _has_vec AND NOT _has_text THEN
        RAISE EXCEPTION
            'pgmnemo.recall_hybrid: both query_embedding and query_text are NULL/empty -- '
            'at least one retrieval signal is required';
    END IF;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    vec_weight  := GREATEST(0.0, LEAST(1.0, vec_weight));
    bm25_weight := GREATEST(0.0, LEAST(1.0, bm25_weight));
    _rrf_k_f    := GREATEST(1.0, rrf_k::DOUBLE PRECISION);
    _raw_blend_weight := 1.0 / (_rrf_k_f + 1.0);

    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN
        _ef_search := 100;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _as_of_ts := NULLIF(current_setting('pgmnemo.as_of_timestamp', TRUE), '')::TIMESTAMPTZ;
    EXCEPTION WHEN OTHERS THEN _as_of_ts := NULL;
    END;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.2)));
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.2;
    END;

    BEGIN
        _conf_boost_w := GREATEST(0.0, LEAST(0.01, COALESCE(
            NULLIF(current_setting('pgmnemo.confidence_boost_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));
    EXCEPTION WHEN OTHERS THEN _conf_boost_w := 0.0;
    END;

    BEGIN
        _bm25_budget_ms := GREATEST(1, COALESCE(
            NULLIF(current_setting('pgmnemo.bm25_budget_ms', TRUE), '')::INT, 250));
    EXCEPTION WHEN OTHERS THEN _bm25_budget_ms := 250;
    END;

    IF _has_text THEN
        _lexical_text := left(trim(query_text), 200);
        BEGIN
            _tsquery := websearch_to_tsquery('simple', _lexical_text);
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', _lexical_text);
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    _fetch_k_vec  := GREATEST(k * 4, _ef_search);
    _fetch_k_bm25 := GREATEST(k * 4, 40);

    -- ── BM25 candidates (unchanged from v0.14.0) ──────────────────────────────
    BEGIN
        CREATE TEMP TABLE _pgmnemo_bm25_work (
            id             BIGINT          PRIMARY KEY,
            raw_bm25_score DOUBLE PRECISION NOT NULL DEFAULT 0.0
        ) ON COMMIT DROP;
    EXCEPTION WHEN duplicate_table THEN
        TRUNCATE TABLE _pgmnemo_bm25_work;
    END;

    IF _has_text THEN
        BEGIN
            EXECUTE format('SET LOCAL statement_timeout = %s', _bm25_budget_ms);

            INSERT INTO _pgmnemo_bm25_work (id, raw_bm25_score)
            SELECT
                al.id,
                ts_rank_cd(al.full_text, _tsquery, 32)::DOUBLE PRECISION
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND al.full_text @@ _tsquery
              AND (_include_unverified OR al.verified_at IS NOT NULL)
              AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
              AND (recall_hybrid.project_id_filter IS NULL
                   OR al.project_id = recall_hybrid.project_id_filter)
              AND (recall_hybrid.exclude_dag_id IS NULL
                   OR al.source_dag_id IS DISTINCT FROM recall_hybrid.exclude_dag_id)
              AND (recall_hybrid.p_content_types IS NULL
                   OR al.content_type = ANY(recall_hybrid.p_content_types))
              AND (_as_of_ts IS NULL
                   OR (al.t_valid_from <= _as_of_ts AND al.t_valid_to > _as_of_ts))
              AND (_as_of_ts IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
            ORDER BY 2 DESC
            LIMIT _fetch_k_bm25;

            EXECUTE 'SET LOCAL statement_timeout = 0';

        EXCEPTION WHEN query_canceled THEN
            _bm25_timed_out := TRUE;
            _has_text       := FALSE;
            RAISE NOTICE
                'pgmnemo.recall_hybrid: BM25 signal exceeded %ms budget — degrading to '
                'vector-only recall. Tune pgmnemo.bm25_budget_ms or shorten query_text.',
                _bm25_budget_ms;
        END;
    END IF;

    -- ── Phase 1: vector candidates — executed as a standalone EXECUTE statement ──
    --
    -- FIX (v0.14.1): When LIMIT is a plpgsql local variable, PostgreSQL compiles
    -- the RETURN QUERY block with a generic, parameter-blind plan that does not
    -- know the LIMIT value.  Without a concrete small-limit hint, the cost model
    -- prefers Seq Scan + top-N heapsort over the HNSW index scan, causing 10–1000×
    -- latency regressions on large corpora (confirmed with EXPLAIN on 3000–7440
    -- row corpus: HNSW cost 448 vs SeqScan 413 in generic plan; SeqScan wins
    -- without a concrete LIMIT, but HNSW wins when the planner sees the value).
    --
    -- Embedding _fetch_k_vec as a literal integer in the SQL text (via format())
    -- lets the planner see the concrete value and reliably choose the HNSW index
    -- scan.  The temp table is ON COMMIT DROP; a duplicate-table exception (same
    -- transaction, re-entrant call) is handled by truncating before reuse.

    BEGIN
        CREATE TEMP TABLE _pgmnemo_vc (
            id             BIGINT,
            role           TEXT,
            project_id     INT,
            topic          TEXT,
            lesson_text    TEXT,
            importance     SMALLINT,
            metadata       JSONB,
            commit_sha     TEXT,
            artifact_hash  TEXT,
            verified_at    TIMESTAMPTZ,
            created_at     TIMESTAMPTZ,
            confidence     REAL,
            raw_vec_score  DOUBLE PRECISION
        ) ON COMMIT DROP;
    EXCEPTION WHEN duplicate_table THEN
        TRUNCATE TABLE _pgmnemo_vc;
    END;

    IF _has_vec THEN
        EXECUTE format($vec_sql$
            INSERT INTO _pgmnemo_vc
            SELECT
                al.id,
                al.role,        al.project_id, al.topic,      al.lesson_text,
                al.importance,  al.metadata,   al.commit_sha, al.artifact_hash,
                al.verified_at, al.created_at, al.confidence,
                (1.0 - (al.embedding <=> $1))::DOUBLE PRECISION  AS raw_vec_score
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND al.embedding IS NOT NULL
              AND ($2 OR al.verified_at IS NOT NULL)
              AND ($3 IS NULL OR al.role = $3)
              AND ($4 IS NULL OR al.project_id = $4)
              AND ($5 IS NULL OR al.source_dag_id IS DISTINCT FROM $5)
              AND ($6 IS NULL OR al.content_type = ANY($6))
              AND ($7 IS NULL OR (al.t_valid_from <= $7 AND al.t_valid_to > $7))
              AND ($7 IS NOT NULL OR al.t_valid_to = 'infinity'::TIMESTAMPTZ)
            ORDER BY al.embedding <=> $1   -- HNSW index scan (literal LIMIT below)
            LIMIT %s                        -- literal value: planner sees k*4 or ef_search
        $vec_sql$, _fetch_k_vec)
        USING query_embedding, _include_unverified, role_filter, project_id_filter,
              exclude_dag_id, p_content_types, _as_of_ts;
    END IF;
    -- If _has_vec = FALSE: _pgmnemo_vc remains empty; all_candidates UNION ALL
    -- below will only contain rows from _pgmnemo_bm25_work.

    RETURN QUERY
    WITH RECURSIVE
    -- Merge: vector candidates (temp table) LEFT JOIN bm25 results + anti-join UNION ALL
    all_candidates AS (
        SELECT
            v.id, v.role, v.project_id, v.topic, v.lesson_text,
            v.importance, v.metadata, v.commit_sha, v.artifact_hash,
            v.verified_at, v.created_at, v.confidence,
            v.raw_vec_score,
            COALESCE(bw.raw_bm25_score, 0.0::DOUBLE PRECISION) AS raw_bm25_score
        FROM _pgmnemo_vc v                            -- temp table (was: vec_candidates CTE)
        LEFT JOIN _pgmnemo_bm25_work bw ON bw.id = v.id

        UNION ALL

        SELECT
            al.id, al.role, al.project_id, al.topic, al.lesson_text,
            al.importance, al.metadata, al.commit_sha, al.artifact_hash,
            al.verified_at, al.created_at, al.confidence,
            0.0::DOUBLE PRECISION AS raw_vec_score,
            bw.raw_bm25_score
        FROM _pgmnemo_bm25_work bw
        JOIN pgmnemo.agent_lesson al ON al.id = bw.id
        WHERE bw.id NOT IN (SELECT id FROM _pgmnemo_vc)
    ),
    -- RRF ranking over bounded candidate set
    rrf_ranked AS (
        SELECT *,
            COUNT(*) OVER ()                                              AS n_candidates,
            ROW_NUMBER() OVER (ORDER BY raw_vec_score DESC NULLS LAST, id ASC) AS vec_rank,
            CASE WHEN raw_bm25_score > 0
                 THEN RANK() OVER (PARTITION BY (raw_bm25_score > 0)
                                   ORDER BY raw_bm25_score DESC NULLS LAST)
                 ELSE NULL
            END                                                           AS bm25_rank_sparse
        FROM all_candidates
    ),
    scored AS (
        SELECT
            r.id, r.role, r.project_id, r.topic, r.lesson_text,
            r.importance, r.metadata, r.commit_sha, r.artifact_hash,
            r.verified_at, r.created_at, r.confidence,
            r.raw_vec_score  AS v_score,
            r.raw_bm25_score AS b_score,
            (vec_weight  / (_rrf_k_f + r.vec_rank::DOUBLE PRECISION)
           + bm25_weight / (_rrf_k_f + COALESCE(r.bm25_rank_sparse,
                                                 r.n_candidates + 1)::DOUBLE PRECISION)
           + _raw_blend_weight * (
                 vec_weight  * r.raw_vec_score
               + bm25_weight * r.raw_bm25_score))
                AS rrf_sparse
        FROM rrf_ranked r
    ),
    anchors AS (
        SELECT id FROM scored ORDER BY rrf_sparse DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    final AS (
        SELECT
            s.id,
            (
                s.rrf_sparse
              + _aux_scale * (
                    0.025 * (s.importance::DOUBLE PRECISION / 5.0)
                  + 0.025 * s.confidence::DOUBLE PRECISION
                  + 0.05 * GREATEST(0.0, 1.0 - LEAST(
                               EXTRACT(EPOCH FROM (NOW() - s.created_at)) / (90.0 * 86400.0), 1.0))
                  + 0.05 * (CASE
                                WHEN s.commit_sha IS NOT NULL AND s.verified_at IS NOT NULL THEN 1.0
                                WHEN s.commit_sha IS NOT NULL                               THEN 0.4
                                ELSE 0.0 END)
                )
              + _conf_boost_w * (s.confidence::DOUBLE PRECISION - 0.5)
            ) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
              AS final_score,
            s.role, s.project_id, s.topic, s.lesson_text, s.importance,
            s.metadata, s.commit_sha, s.artifact_hash, s.verified_at, s.created_at,
            s.confidence, s.v_score, s.b_score, s.rrf_sparse,
            COALESCE(gp.proximity, 0.0) AS prox
        FROM scored s
        LEFT JOIN graph_proximity gp ON gp.lesson_id = s.id
    ),
    final_results AS MATERIALIZED (
        SELECT
            f.id                   AS lesson_id,
            f.final_score          AS score,
            f.v_score              AS vec_score,
            f.b_score              AS bm25_score,
            f.rrf_sparse           AS rrf_score,
            f.role,
            f.project_id,
            f.topic,
            f.lesson_text,
            f.importance,
            f.metadata,
            f.commit_sha,
            f.artifact_hash,
            f.verified_at,
            f.created_at,
            f.confidence::REAL,
            LEAST(1.0, GREATEST(0.0, f.v_score))::REAL AS match_confidence
        FROM final f
        WHERE (p_min_score IS NULL
               OR LEAST(1.0, GREATEST(0.0, f.v_score))::REAL >= p_min_score)
        ORDER BY f.final_score DESC, f.id ASC
        LIMIT k
    ),
    _stamp AS (
        UPDATE pgmnemo.agent_lesson
        SET last_recalled_at = NOW(),
            recall_count     = recall_count + 1
        WHERE id = ANY(ARRAY(SELECT lesson_id FROM final_results))
          AND COALESCE(
              NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN,
              TRUE)
        RETURNING id
    )
    SELECT
        fr.lesson_id, fr.score, fr.vec_score, fr.bm25_score, fr.rrf_score,
        fr.role, fr.project_id, fr.topic, fr.lesson_text, fr.importance,
        fr.metadata, fr.commit_sha, fr.artifact_hash, fr.verified_at, fr.created_at,
        fr.confidence, fr.match_confidence
    FROM final_results fr
    ORDER BY fr.score DESC, fr.lesson_id ASC;

    IF NOT FOUND AND p_min_score IS NULL THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.t_valid_to = 'infinity'::TIMESTAMPTZ
          AND al.verified_at IS NULL
          AND (recall_hybrid.role_filter IS NULL OR al.role = recall_hybrid.role_filter)
          AND (recall_hybrid.project_id_filter IS NULL
               OR al.project_id = recall_hybrid.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % matching lesson(s) are unverified (ingested without commit_sha/artifact_hash) '
                'and excluded by default. SET pgmnemo.include_unverified = ''on'' for this session, '
                'or pass provenance on ingest.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_hybrid(vector, TEXT, INT, TEXT, INT, DOUBLE PRECISION, DOUBLE PRECISION, INT, TEXT, text[], REAL) IS
    'v0.14.1 — HNSW planner regression fix. Vector candidates now fetched via '
    'EXECUTE with a literal LIMIT so the planner always sees the concrete value '
    'and chooses the HNSW index scan instead of Seq Scan + top-N heapsort. '
    'Confirmed on 3000–7440 row corpus: HNSW cost 448 vs SeqScan 413 in generic plan; '
    'literal LIMIT restores HNSW selection. Scoring, RRF, and recency stamp unchanged. '
    'v0.13.0 — Outcome Loop v2: adds p_min_score REAL DEFAULT NULL (11th param). '
    'p_min_score: filter rows where match_confidence (= vec_score clamped to [0,1]) < p_min_score. '
    'NULL = no filter (backward-compatible). Ghost-lesson NOTICE fires only when p_min_score IS NULL. '
    'Inherits v0.11.0 (RFC-001 §D2 / P0.2: typed recall) and v0.10.1 (#87) fixes: '
    'query_text cap, indexed full_text BM25, bm25_budget_ms timeout, simple tsconfig. '
    'match_confidence: vec_score (cosine similarity, [0,1]). '
    'RRF fusion is sparse-safe per Cormack 2009: unmatched candidates rank n_candidates+1. '
    'graph_proximity via mem_edge causal/temporal walk (depth ≤5). '
    'VOLATILE (side-effects: recency stamp, temp tables _pgmnemo_bm25_work, _pgmnemo_vc).';

-- §7: recall_fast — add p_min_score (7th param)
--
-- Drop old 6-param overload to prevent ambiguous-function errors.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_fast(vector, INT, TEXT, INT, TEXT, TEXT[]);

CREATE OR REPLACE FUNCTION pgmnemo.recall_fast(
    query_embedding   vector(1024),
    k                 INT     DEFAULT 10,
    role_filter       TEXT    DEFAULT NULL,
    project_id_filter INT     DEFAULT NULL,
    exclude_dag_id    TEXT    DEFAULT NULL,
    p_content_types   TEXT[]  DEFAULT NULL,
    p_min_score       REAL    DEFAULT NULL  -- NEW
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE plpgsql
VOLATILE
AS $$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _track_recency      BOOLEAN;
BEGIN
    -- Set HNSW ef_search from GUC (same pattern as recall_hybrid)
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    BEGIN
        _track_recency := COALESCE(
            NULLIF(current_setting('pgmnemo.track_recall_recency', TRUE), '')::BOOLEAN, TRUE);
    EXCEPTION WHEN OTHERS THEN _track_recency := TRUE;
    END;

    -- #84: reject NULL query_embedding early — HNSW-only path has no text fallback.
    -- recall_hybrid() accepts NULL query_embedding when query_text is present; recall_fast
    -- is vector-only and cannot fall back to BM25, so NULL embedding is always an error.
    IF query_embedding IS NULL THEN
        RAISE EXCEPTION
            'pgmnemo.recall_fast: query_embedding IS NULL -- '
            'a vector embedding is required for HNSW search. '
            'recall_fast has no text-only fallback; use recall_hybrid() '
            'if you have query_text but no embedding.';
    END IF;

    RETURN QUERY
    WITH fast_ranked AS (
        SELECT
            al.id,
            (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION AS vec_score,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND al.embedding IS NOT NULL
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_fast.role_filter IS NULL
               OR al.role = recall_fast.role_filter)
          AND (recall_fast.project_id_filter IS NULL
               OR al.project_id = recall_fast.project_id_filter)
          AND (recall_fast.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_fast.exclude_dag_id)
          -- P0.2: typed recall pushdown (ix_pgmnemo_content_type_active)
          AND (recall_fast.p_content_types IS NULL
               OR al.content_type = ANY(recall_fast.p_content_types))
        ORDER BY al.embedding <=> query_embedding
        LIMIT k
    ),
    stamped AS (
        UPDATE pgmnemo.agent_lesson al2
        SET
            last_recalled_at = NOW(),
            recall_count     = al2.recall_count + 1
        FROM fast_ranked fr
        WHERE al2.id = fr.id
          AND _track_recency
        RETURNING al2.id
    )
    SELECT
        fr.id          AS lesson_id,
        fr.vec_score   AS score,
        fr.role,
        fr.project_id,
        fr.topic,
        fr.lesson_text,
        fr.importance,
        fr.metadata,
        fr.commit_sha,
        fr.artifact_hash,
        fr.verified_at,
        fr.created_at
    FROM fast_ranked fr
    -- stamped CTE is a side-effect sink; reference it to prevent optimiser elision
    LEFT JOIN stamped s ON s.id = fr.id
    WHERE (p_min_score IS NULL OR fr.vec_score::REAL >= p_min_score)
    ORDER BY fr.vec_score DESC;
END;
$$;

COMMENT ON FUNCTION pgmnemo.recall_fast(vector, INT, TEXT, INT, TEXT, TEXT[], REAL) IS
    'HNSW-only vector recall — O(k log n), no BM25/graph/RRF. '
    'Uses ORDER BY embedding <=> query LIMIT k to activate the HNSW index. '
    'score = cosine similarity (1 - distance). '
    'Respects include_unverified, ef_search, track_recall_recency GUCs. '
    'Filters: role_filter, project_id_filter, exclude_dag_id (same as recall_hybrid). '
    'v0.10.0: default MCP recall path. Use recall_hybrid for full 6-signal fusion. '
    'v0.10.1 #84: raises EXCEPTION when query_embedding IS NULL (no text-only fallback). '
    'v0.11.1: p_content_types TEXT[] DEFAULT NULL (6th param) — typed recall pushdown '
    'using ix_pgmnemo_content_type_active. NULL=all types (backward-compatible). '
    'Non-NULL restricts candidates to the given content_type values before HNSW ranking. '
    'v0.13.0: p_min_score REAL DEFAULT NULL (7th param) — post-rank filter on vec_score.';

-- §8: recall_lessons — add p_min_score (9th param)
--
-- Drop old 8-param overload to prevent ambiguous-function errors.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ, TEXT, TEXT[]);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons(
    query_embedding   vector(1024),
    k                 INT         DEFAULT 10,
    role_filter       TEXT        DEFAULT NULL,
    project_id_filter INT         DEFAULT NULL,
    query_text        TEXT        DEFAULT NULL,
    as_of_ts          TIMESTAMPTZ DEFAULT NULL,
    exclude_dag_id    TEXT        DEFAULT NULL,
    p_content_types   TEXT[]      DEFAULT NULL,
    p_min_score       REAL        DEFAULT NULL  -- NEW
)
RETURNS TABLE (
    lesson_id        BIGINT,
    score            DOUBLE PRECISION,
    role             TEXT,
    project_id       INT,
    topic            TEXT,
    lesson_text      TEXT,
    importance       SMALLINT,
    metadata         JSONB,
    commit_sha       TEXT,
    artifact_hash    TEXT,
    verified_at      TIMESTAMPTZ,
    created_at       TIMESTAMPTZ,
    vec_score        DOUBLE PRECISION,
    bm25_score       DOUBLE PRECISION,
    rrf_score        DOUBLE PRECISION,
    confidence       REAL,
    match_confidence REAL
)
LANGUAGE plpgsql
VOLATILE
AS $func$
#variable_conflict use_column
DECLARE
    _ef_search          INT;
    _include_unverified BOOLEAN;
    _tsquery            TSQUERY;
    _has_text           BOOLEAN;
    _has_vec            BOOLEAN;
    _gamma              DOUBLE PRECISION;
    _temporal_boost     DOUBLE PRECISION;
    _graph_weight       DOUBLE PRECISION;
    _disable_hybrid     BOOLEAN;
    _max_depth          CONSTANT INT := 5;
    _max_chars          INT;
    _query_text         TEXT;
    _ghost_count        INT;
BEGIN
    _max_chars := COALESCE(
        NULLIF(current_setting('pgmnemo.max_query_text_chars', TRUE), '')::INT, 2000);
    IF query_text IS NOT NULL AND length(query_text) > _max_chars THEN
        RAISE NOTICE 'pgmnemo.recall_lessons: query_text truncated to % chars. Original: %',
                     _max_chars, length(query_text);
        _query_text := left(query_text, _max_chars);
    ELSE
        _query_text := query_text;
    END IF;

    _has_vec  := query_embedding IS NOT NULL;
    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;

    IF NOT _has_vec AND _has_text THEN
        RAISE NOTICE
            'pgmnemo: query_embedding IS NULL -- falling back to text-only recall; no semantic similarity';
    END IF;

    BEGIN
        _disable_hybrid := COALESCE(
            current_setting('pgmnemo.disable_hybrid', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _disable_hybrid := FALSE;
    END;

    -- Hybrid path delegates to recall_hybrid (which handles stamping + exclude_dag_id)
    -- P0.2: p_content_types forwarded as the 10th argument of recall_hybrid().
    -- v0.13.0: p_min_score forwarded as the 11th argument of recall_hybrid().
    IF NOT _disable_hybrid AND _has_vec AND _has_text THEN
        IF as_of_ts IS NOT NULL THEN
            PERFORM set_config('pgmnemo.as_of_timestamp', as_of_ts::TEXT, TRUE);
        END IF;

        RETURN QUERY
        SELECT
            h.lesson_id, h.score, h.role, h.project_id, h.topic, h.lesson_text,
            h.importance, h.metadata, h.commit_sha, h.artifact_hash,
            h.verified_at, h.created_at,
            h.vec_score, h.bm25_score, h.rrf_score,
            h.confidence, h.match_confidence
        FROM pgmnemo.recall_hybrid(
            query_embedding, _query_text, k,
            role_filter, project_id_filter, 0.4, 0.4, 60,
            exclude_dag_id, p_content_types, p_min_score  -- pass p_min_score
        ) h;
        RETURN;
    END IF;

    -- Vector-only path (pgmnemo.disable_hybrid = 'true' or no query_text)
    BEGIN
        _ef_search := COALESCE(
            NULLIF(current_setting('pgmnemo.ef_search', TRUE), '')::INT, 100);
        IF _ef_search BETWEEN 10 AND 500 THEN
            EXECUTE format('SET LOCAL pgvector.hnsw.ef_search = %s', _ef_search);
        END IF;
    EXCEPTION WHEN OTHERS THEN NULL;
    END;

    BEGIN
        _include_unverified := COALESCE(
            current_setting('pgmnemo.include_unverified', TRUE)::BOOLEAN, FALSE);
    EXCEPTION WHEN OTHERS THEN _include_unverified := FALSE;
    END;

    _gamma := COALESCE(
        NULLIF(current_setting('pgmnemo.recency_weight', TRUE), '')::DOUBLE PRECISION, 0.05);
    _temporal_boost := GREATEST(0.0, LEAST(20.0, COALESCE(
        NULLIF(current_setting('pgmnemo.temporal_boost', TRUE), '')::DOUBLE PRECISION, 1.0)));
    _gamma := _gamma * _temporal_boost;

    BEGIN
        _graph_weight := GREATEST(0.0, LEAST(0.5, COALESCE(
            NULLIF(current_setting('pgmnemo.graph_proximity_weight', TRUE), '')::DOUBLE PRECISION,
            0.0)));  -- Fix 5: OPT-IN default (was 0.2)
    EXCEPTION WHEN OTHERS THEN _graph_weight := 0.0;  -- Fix 5: OPT-IN default
    END;

    _has_text := _query_text IS NOT NULL AND length(trim(_query_text)) > 0;
    IF _has_text THEN
        BEGIN
            _tsquery := websearch_to_tsquery('simple', left(trim(_query_text), 200));  -- Fix 4+1
        EXCEPTION WHEN OTHERS THEN
            BEGIN
                _tsquery := plainto_tsquery('simple', left(trim(_query_text), 200));   -- Fix 4+1
            EXCEPTION WHEN OTHERS THEN _has_text := FALSE;
            END;
        END;
    END IF;

    RETURN QUERY
    WITH RECURSIVE
    candidates AS (
        SELECT
            al.id,
            al.role,
            al.project_id,
            al.topic,
            al.lesson_text,
            al.importance,
            al.metadata,
            al.commit_sha,
            al.artifact_hash,
            al.verified_at,
            al.created_at,
            al.confidence,
            CASE
                WHEN al.embedding IS NOT NULL
                THEN (1.0 - (al.embedding <=> query_embedding))::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS vec_score,
            CASE
                WHEN _has_text AND al.full_text @@ _tsquery     -- Fix 2: indexed full_text
                THEN ts_rank_cd(al.full_text, _tsquery)::DOUBLE PRECISION
                ELSE 0.0::DOUBLE PRECISION
            END AS ft_score
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active
          AND (_include_unverified OR al.verified_at IS NOT NULL)
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL
               OR al.project_id = recall_lessons.project_id_filter)
          AND (recall_lessons.exclude_dag_id IS NULL
               OR al.source_dag_id IS DISTINCT FROM recall_lessons.exclude_dag_id)
          AND (al.embedding IS NOT NULL OR _has_text)
          -- P0.2: typed recall pushdown (ix_pgmnemo_content_type_active)
          AND (recall_lessons.p_content_types IS NULL
               OR al.content_type = ANY(recall_lessons.p_content_types))
    ),
    anchors AS (
        SELECT id FROM candidates ORDER BY vec_score DESC LIMIT 5
    ),
    graph_walk(anchor_id, depth, reached_id) AS (
        SELECT id, 0, id FROM anchors WHERE _graph_weight > 0  -- Fix 5
        UNION ALL
        SELECT gw.anchor_id, gw.depth + 1, me.target_id
        FROM graph_walk gw
        JOIN pgmnemo.mem_edge me ON me.source_id = gw.reached_id
        WHERE me.edge_kind IN ('causal', 'temporal')
          AND gw.depth < _max_depth
    ),
    graph_proximity AS (
        SELECT gw.reached_id AS lesson_id,
               MAX(1.0 - gw.depth::DOUBLE PRECISION / _max_depth::DOUBLE PRECISION) AS proximity
        FROM graph_walk gw WHERE gw.depth > 0 GROUP BY gw.reached_id
    ),
    scored AS (
        SELECT
            c.id, c.role, c.project_id, c.topic, c.lesson_text, c.importance,
            c.metadata, c.commit_sha, c.artifact_hash, c.verified_at, c.created_at,
            c.confidence,
            c.vec_score,
            c.ft_score,
            (c.vec_score + _gamma * GREATEST(0.0, 1.0 - LEAST(
                 EXTRACT(EPOCH FROM (NOW() - c.created_at)) / (90.0 * 86400.0), 1.0
             ))) * (1.0 + _graph_weight * COALESCE(gp.proximity, 0.0))
             + c.ft_score * 0.1
             AS combined_score
        FROM candidates c
        LEFT JOIN graph_proximity gp ON gp.lesson_id = c.id
    )
    SELECT
        s.id                  AS lesson_id,
        s.combined_score      AS score,
        s.role,
        s.project_id,
        s.topic,
        s.lesson_text,
        s.importance,
        s.metadata,
        s.commit_sha,
        s.artifact_hash,
        s.verified_at,
        s.created_at,
        s.vec_score,
        s.ft_score            AS bm25_score,
        0.0::DOUBLE PRECISION AS rrf_score,
        s.confidence::REAL,
        LEAST(1.0, GREATEST(0.0, s.vec_score))::REAL AS match_confidence
    FROM scored s
    WHERE (p_min_score IS NULL
           OR LEAST(1.0, GREATEST(0.0, s.vec_score))::REAL >= p_min_score)
    ORDER BY s.combined_score DESC, s.id ASC
    LIMIT k;

    IF NOT FOUND THEN
        SELECT COUNT(*)::INT INTO _ghost_count
        FROM pgmnemo.agent_lesson al
        WHERE al.is_active AND al.t_valid_to = 'infinity'::TIMESTAMPTZ AND al.verified_at IS NULL
          AND (recall_lessons.role_filter IS NULL OR al.role = recall_lessons.role_filter)
          AND (recall_lessons.project_id_filter IS NULL
               OR al.project_id = recall_lessons.project_id_filter);
        IF _ghost_count > 0 THEN
            RAISE NOTICE
                'pgmnemo: % unverified lesson(s) excluded. '
                'SET pgmnemo.include_unverified = ''on'' to include them.',
                _ghost_count;
        END IF;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.recall_lessons(vector, INT, TEXT, INT, TEXT, TIMESTAMPTZ, TEXT, TEXT[], REAL) IS
    'v0.13.0 hybrid router with diagnostic columns, typed recall, and min_score gate. '
    'Routes to recall_hybrid() when both query_embedding and query_text are present '
    '(and pgmnemo.disable_hybrid is FALSE/unset). '
    'Falls back to vector-only (HNSW + recency + graph) when query_text is absent. '
    'p_content_types TEXT[] DEFAULT NULL (8th param, v0.11.1): typed recall pushdown. '
    'p_min_score REAL DEFAULT NULL (9th param, v0.13.0): filter rows where '
    'match_confidence (vec_score clamped [0,1]) < p_min_score. NULL=no filter. '
    'On the hybrid path: both p_content_types and p_min_score forwarded to recall_hybrid(). '
    'On the vector-only path: p_min_score applied as WHERE filter on match_confidence. '
    'GIN-indexed for BM25 retrieval via ts_rank_cd in recall_hybrid(). '
    'Respects pgmnemo.disable_hybrid, ef_search, include_unverified, recency_weight, '
    'temporal_boost, graph_proximity_weight, max_query_text_chars GUCs.';

-- §9: recall_lessons_pooled — add p_min_score (4th param)
--
-- Drop old 3-param overload.
-- ─────────────────────────────────────────────────────────────────────────────

DROP FUNCTION IF EXISTS pgmnemo.recall_lessons_pooled(vector, INT, INT);

CREATE OR REPLACE FUNCTION pgmnemo.recall_lessons_pooled(
    query_embedding  vector(1024),
    k                INT              DEFAULT 10,
    app_id           INT              DEFAULT NULL,
    p_min_score      REAL             DEFAULT NULL  -- NEW
)
RETURNS TABLE (
    lesson_id     BIGINT,
    score         DOUBLE PRECISION,
    role          TEXT,
    project_id    INT,
    topic         TEXT,
    lesson_text   TEXT,
    importance    SMALLINT,
    metadata      JSONB,
    commit_sha    TEXT,
    artifact_hash TEXT,
    verified_at   TIMESTAMPTZ,
    created_at    TIMESTAMPTZ
)
LANGUAGE sql
STABLE
PARALLEL SAFE
AS $$
    SELECT lesson_id, score, role, project_id, topic, lesson_text,
           importance, metadata, commit_sha, artifact_hash, verified_at, created_at
    FROM pgmnemo.recall_lessons(query_embedding, k, NULL::TEXT, app_id, NULL::TEXT, NULL::TIMESTAMPTZ, NULL::TEXT, NULL::TEXT[], p_min_score);
$$;

COMMENT ON FUNCTION pgmnemo.recall_lessons_pooled(vector, INT, INT, REAL) IS
    'v0.13.0: Cross-role recall wrapper: calls recall_lessons() with role=NULL (pooled). '
    'p_min_score: filter rows where match_confidence < p_min_score (NULL=no filter). '
    'DEPRECATED 3-param form: use 4-param form.';

-- End of pgmnemo--0.12.2--0.13.0.sql
-- pgmnemo--0.13.0--0.14.0.sql
-- Upgrade: 0.13.0 → 0.14.0 — Corpus self-maintenance: classify-on-write
-- SPDX-License-Identifier: Apache-2.0

-- ─────────────────────────────────────────────────────────────────────────────
-- §1  classify_content_type(p_text text) → text
-- ─────────────────────────────────────────────────────────────────────────────
-- Deterministic keyword/regex content-type classifier.  NO LLM.
-- Categories (evaluated in priority order — first match wins):
--
--   incident  — failure / outage / root-cause / postmortem context
--   decision  — explicit choice, agreement, ADR, verdict
--   entity    — person, service, project, component descriptor
--   fact      — stable state assertion (no instructional verbs present)
--   procedure — how-to, steps, commands (broad catch-all)
--   NULL      — no confident match; caller may keep existing label
--
-- The function is IMMUTABLE (no side effects, result depends only on input)
-- and STRICT (NULL input → NULL output, no exception).
-- Uses POSIX case-insensitive regex (~*) with ARE word-boundary anchors
-- (\m = word start, \M = word end).
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.classify_content_type(p_text text)
RETURNS text
LANGUAGE sql
IMMUTABLE
STRICT
AS $func$
    SELECT CASE

        -- ── INCIDENT (highest priority) ────────────────────────────────────
        -- Explicit incident / postmortem / outage markers
        WHEN p_text ~* '\m(root.?cause|postmortem|outage|incident|hotfix|regression|rollback|revert)\M'
          OR p_text ~* '\m(stacktrace|traceback|segfault|oom.?kill)\M'
          -- Failure word + causation word together (describes *what happened*, not *what to do*)
          OR (p_text ~* '\m(crashed?|broken|corrupted?|panicked?|failed)\M'
              AND p_text ~* '\m(caused|because of|due to|led to|resulted?|triggered?)\M')
          -- Specific bug-report language
          OR p_text ~* '\mthe (bug|error|issue|failure) (was|is)\M'
          -- NOTE: \mbugfix\M removed in v0.14.1 — caused false-positives on process
          -- text like "When submitting a bugfix PR, always …" (should be 'procedure').
          -- Genuine bugfix incidents still fire via root.?cause / postmortem / outage /
          -- regression / hotfix or the failure+causation compound above.
        THEN 'incident'

        -- ── DECISION ────────────────────────────────────────────────────────
        -- ADR reference or explicit decision/agreement vocabulary
        WHEN p_text ~* '\mADR[-\s]?\d'
          OR p_text ~* '\m(decided|decision|verdict|approved|rejected|chosen)\M'
          OR p_text ~* 'we (chose|selected|adopted|agreed|preferred?|decided)\M'
          -- Comparative selection language (chose X over Y / instead of Y)
          OR (p_text ~* '\m(chose|selected|adopted)\M'
              AND p_text ~* '\m(instead|over|rather than|in favor of)\M')
        THEN 'decision'

        -- ── ENTITY ──────────────────────────────────────────────────────────
        -- Describes what something IS (person, service, system), not what to do.
        -- Allow optional modifier adjectives between "is (a|the)" and the type word.
        WHEN p_text ~* '\mis (?:a|the)(?:\s+\w+){0,2}\s+(?:service|tool|library|module|package|repo|repository|component|system|project)\M'
          OR p_text ~* '\mis (?:the)(?:\s+\w+){0,1}\s+(?:lead|owner|author|contact|maintainer|engineer|manager|architect|operator|po)\M'
          OR p_text ~* '\m(owns?|maintains?|is responsible for)\M'
        THEN 'entity'

        -- ── FACT ────────────────────────────────────────────────────────────
        -- Stable assertion about configuration / schema / state.
        -- Excluded when instructional verbs or "when" are present.
        WHEN (   p_text ~* '\m(defaults? to|default (value |port |behavior )?is)\M'
              OR p_text ~* '\malways returns?\M'
              OR p_text ~* '\m(is always|is set to|is stored in|is fixed at|is never)\M'
              OR p_text ~* '\mversion (is|was)\M'
              OR p_text ~* '\m(schema|table|column|port|endpoint) (is|has|was|uses?|contains?)\M'
             )
          AND p_text !~* '\mwhen\M'
          AND p_text !~* '\m(run|install|deploy|configure|restart|use)\M'
        THEN 'fact'

        -- ── PROCEDURE (catch-all) ────────────────────────────────────────────
        -- How-to, operational advice, commands, patterns
        WHEN p_text ~* '\mwhen\M'
          OR p_text ~* '\m(must|should|always|never|avoid|prefer|recommend)\M'
          OR p_text ~* '\m(run|execute|install|deploy|configure|restart|use)\M'
          OR p_text ~* '\m(command|script|workflow|pattern|approach|method)\M'
          OR p_text ~* '\mhow to\M'
        THEN 'procedure'

        -- NULL: no confident match — caller keeps whatever label exists
        ELSE NULL
    END
$func$;

COMMENT ON FUNCTION pgmnemo.classify_content_type(text) IS
    'Deterministic keyword/regex content-type classifier (v0.14.0). No LLM. '
    'Returns: procedure | incident | decision | fact | entity | NULL. '
    'Priority: incident > decision > entity > fact > procedure > NULL. '
    'IMMUTABLE + STRICT: NULL input → NULL output; result is purely a function of the text. '
    'Used by ingest() auto-fill and reclassify_corpus().';


-- ─────────────────────────────────────────────────────────────────────────────
-- §2  ingest() — add p_content_type with auto-fill (v0.14.0)
-- ─────────────────────────────────────────────────────────────────────────────
-- Strategy:
--   • Drop the old 9-param signature (TEXT,INT,TEXT,TEXT,SMALLINT,vector,TEXT,TEXT,JSONB).
--   • Create a new 10-param signature with p_content_type TEXT DEFAULT NULL as the last param.
--   • Existing 9-arg call sites match the new function via the default — backward compatible.
--   • When p_content_type IS NULL → auto-derive via classify_content_type(p_lesson_text).
--   • When p_content_type is supplied → use it verbatim; never override an explicit caller value.
--   • All existing quality gates (F1 min-length, F2 repetition, F3 near-dup) are preserved.
--
-- NOTE: DROP works here because this script runs inside ALTER EXTENSION pgmnemo UPDATE,
-- which temporarily removes object-extension membership for the duration of the upgrade.
-- ─────────────────────────────────────────────────────────────────────────────

-- Remove old 9-param overload so the 10-param (with DEFAULT NULL) is unambiguous.
DROP FUNCTION IF EXISTS pgmnemo.ingest(TEXT, INT, TEXT, TEXT, SMALLINT, vector, TEXT, TEXT, JSONB);

CREATE FUNCTION pgmnemo.ingest(
    p_role           TEXT,
    p_project_id     INT,
    p_topic          TEXT,
    p_lesson_text    TEXT,
    p_importance     SMALLINT     DEFAULT 3,
    p_embedding      vector(1024) DEFAULT NULL,
    p_commit_sha     TEXT         DEFAULT NULL,
    p_artifact_hash  TEXT         DEFAULT NULL,
    p_metadata       JSONB        DEFAULT '{}'::jsonb,
    p_content_type   TEXT         DEFAULT NULL     -- NEW: auto-derived when NULL
) RETURNS BIGINT
LANGUAGE plpgsql AS $func$
DECLARE
    new_id                  BIGINT;
    _content_hash           TEXT;
    _prior_count            INT;
    _dedup_id               BIGINT;
    _dedup_sim              DOUBLE PRECISION;
    _tokens                 TEXT[];
    _token                  TEXT;
    _token_counts           JSONB;
    _max_freq               INT;
    _total_tokens           INT;
    _trimmed_text           TEXT;
    _effective_content_type TEXT;
BEGIN
    -- F1: minimum length guard
    IF p_lesson_text IS NULL OR length(trim(p_lesson_text)) < 20 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: lesson_text too short (min 20 chars)';
    END IF;

    -- F2: token-frequency repetition guard
    _trimmed_text := trim(p_lesson_text);
    _tokens := regexp_split_to_array(
        regexp_replace(_trimmed_text, '\s+', ' ', 'g'), ' ');
    _total_tokens := array_length(_tokens, 1);

    IF _total_tokens > 0 THEN
        _token_counts := '{}'::JSONB;
        FOREACH _token IN ARRAY _tokens LOOP
            _token_counts := jsonb_set(
                _token_counts,
                ARRAY[_token],
                to_jsonb(COALESCE((_token_counts->>_token)::INT, 0) + 1)
            );
        END LOOP;

        SELECT MAX(value::INT)
        INTO _max_freq
        FROM jsonb_each_text(_token_counts);

        IF _max_freq::DOUBLE PRECISION / _total_tokens::DOUBLE PRECISION > 0.8 THEN
            RAISE EXCEPTION 'pgmnemo.ingest: lesson_text appears to be repetitive content';
        END IF;
    END IF;

    -- Embedding dimension guard
    IF p_embedding IS NOT NULL AND vector_dims(p_embedding) <> 1024 THEN
        RAISE EXCEPTION 'pgmnemo.ingest: embedding dimension mismatch -- expected 1024, got %',
            vector_dims(p_embedding);
    END IF;

    -- F3: near-duplicate embedding guard
    IF p_embedding IS NOT NULL THEN
        SELECT id, (1.0 - (embedding <=> p_embedding))
        INTO _dedup_id, _dedup_sim
        FROM pgmnemo.agent_lesson
        WHERE is_active
          AND t_valid_to = 'infinity'::TIMESTAMPTZ
          AND embedding IS NOT NULL
          AND project_id = p_project_id
          AND (1.0 - (embedding <=> p_embedding)) > 0.98
        ORDER BY embedding <=> p_embedding
        LIMIT 1;

        IF FOUND THEN
            RAISE WARNING
                'pgmnemo.ingest: near-duplicate detected -- cosine similarity % > 0.98 '
                'to existing lesson_id=% (project_id=%). Returning existing lesson_id.',
                ROUND(_dedup_sim::NUMERIC, 4), _dedup_id, p_project_id;
            RETURN _dedup_id;
        END IF;
    END IF;

    -- Resolve effective content_type:
    --   caller-supplied value always wins; NULL → auto-derive via classifier
    _effective_content_type := COALESCE(
        p_content_type,
        pgmnemo.classify_content_type(p_lesson_text)
    );

    -- Bitemporal dedup observability
    _content_hash := MD5(
        COALESCE(p_role, '')  || '|' ||
        COALESCE(p_topic, '') || '|' ||
        COALESCE(p_commit_sha, COALESCE(p_artifact_hash, ''))
    );

    SELECT COUNT(*)::INT INTO _prior_count
    FROM pgmnemo.agent_lesson
    WHERE content_hash = _content_hash
      AND t_valid_to   = 'infinity'::TIMESTAMPTZ;

    INSERT INTO pgmnemo.agent_lesson (
        role, project_id, topic, lesson_text, importance, embedding,
        commit_sha, artifact_hash, metadata, content_type, verified_at
    ) VALUES (
        p_role, p_project_id, p_topic, p_lesson_text, p_importance, p_embedding,
        p_commit_sha, p_artifact_hash, p_metadata, _effective_content_type,
        NOW()
    ) RETURNING id INTO new_id;

    IF _prior_count > 0 THEN
        RAISE NOTICE
            'pgmnemo.ingest: bitemporal close+create fired -- closed % prior version(s) '
            '(content_hash=%). New lesson_id=%.',
            _prior_count, _content_hash, new_id;
    END IF;

    RETURN new_id;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.ingest(TEXT, INT, TEXT, TEXT, SMALLINT, vector, TEXT, TEXT, JSONB, TEXT) IS
    'Validated public write API v0.14.0. '
    'F1 (min-length): RAISE EXCEPTION when lesson_text < 20 chars. '
    'F2 (repetition): RAISE EXCEPTION when most-frequent token > 80%% of all tokens. '
    'F3 (dedup-warn): RAISE WARNING + RETURN existing lesson_id when cosine_sim > 0.98. '
    'v0.9.0: verified_at = NOW() for all lessons passing quality gates. '
    'v0.14.0: p_content_type (10th param, DEFAULT NULL) — when NULL, auto-derived via '
    'classify_content_type(lesson_text). Explicit caller value always wins. '
    'Provenance tier (commit_sha, artifact_hash) still contributes to ranking aux score.';


-- ─────────────────────────────────────────────────────────────────────────────
-- §3  reclassify_corpus(p_dry_run, p_limit) → TABLE(phase, content_type, cnt)
-- ─────────────────────────────────────────────────────────────────────────────
-- Re-derives content_type for all active lessons using classify_content_type().
--
-- p_dry_run  DEFAULT true  — return before/after distribution WITHOUT writing.
--                            Set to false to commit the UPDATE.
-- p_limit    DEFAULT NULL  — cap on rows to inspect/update (NULL = all rows).
--
-- Return schema:
--   phase        TEXT    — 'before' | 'after' (dry-run) or 'updated' (live)
--   content_type TEXT    — label value (NULL for unlabelled rows)
--   cnt          BIGINT  — row count
--
-- Dry-run "after" semantics:
--   For each row, proposed = COALESCE(classify(lesson_text), current_content_type).
--   Rows where the classifier returns NULL keep their existing label.
--
-- Live semantics (p_dry_run=false):
--   UPDATE content_type only when classify() returns non-NULL AND differs from current.
--   Returns counts by new content_type for the updated rows.
--
-- RETURN QUERY precedes each WITH clause — plpgsql requires RETURN QUERY to be
-- the first keyword of the statement, before any CTE definitions.
-- ─────────────────────────────────────────────────────────────────────────────

CREATE OR REPLACE FUNCTION pgmnemo.reclassify_corpus(
    p_dry_run  boolean DEFAULT true,
    p_limit    int     DEFAULT NULL
)
RETURNS TABLE (phase text, content_type text, cnt bigint)
LANGUAGE plpgsql AS $func$
BEGIN
    IF p_dry_run THEN
        -- ── DRY-RUN: compute distributions, no writes ──────────────────────
        -- Multi-level CTE: candidates → before_dist + after_dist → UNION ALL
        RETURN QUERY
        WITH candidates AS (
            SELECT
                al.content_type                                AS old_ct,
                pgmnemo.classify_content_type(al.lesson_text) AS new_ct
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
            ORDER BY al.id
            LIMIT p_limit
        ),
        -- BEFORE: current distribution
        before_dist AS (
            SELECT 'before'::text AS ph, c.old_ct AS ct, COUNT(*)::bigint AS n
            FROM candidates c
            GROUP BY c.old_ct
        ),
        -- AFTER: distribution if the live run were applied
        -- proposed label wins when non-NULL; else existing label is kept
        after_dist AS (
            SELECT 'after'::text AS ph,
                   COALESCE(c.new_ct, c.old_ct) AS ct,
                   COUNT(*)::bigint AS n
            FROM candidates c
            GROUP BY COALESCE(c.new_ct, c.old_ct)
        )
        SELECT bd.ph, bd.ct, bd.n FROM before_dist bd
        UNION ALL
        SELECT ad.ph, ad.ct, ad.n FROM after_dist ad
        ORDER BY 1, 3 DESC;

    ELSE
        -- ── LIVE RUN: UPDATE rows with a new (different) classifier label ───
        -- RETURN QUERY precedes the WITH so the DML CTE runs in the same statement.
        RETURN QUERY
        WITH to_update AS (
            SELECT al.id,
                   pgmnemo.classify_content_type(al.lesson_text) AS new_ct
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
            ORDER BY al.id
            LIMIT p_limit
        ),
        changed AS (
            UPDATE pgmnemo.agent_lesson al
            SET content_type = tu.new_ct,
                updated_at   = NOW()
            FROM to_update tu
            WHERE al.id = tu.id
              AND tu.new_ct IS NOT NULL
              AND (al.content_type IS DISTINCT FROM tu.new_ct)
            RETURNING tu.new_ct AS final_ct
        )
        SELECT 'updated'::text,
               ch.final_ct,
               COUNT(*)::bigint
        FROM changed ch
        GROUP BY ch.final_ct
        ORDER BY 3 DESC;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reclassify_corpus(boolean, int) IS
    'Corpus self-maintenance: re-derive content_type for active lessons (v0.14.0). '
    'p_dry_run=true (default): returns before/after distributions without writing. '
    'p_dry_run=false: UPDATEs rows where classify_content_type() returns a non-NULL new label. '
    'p_limit: caps the number of rows inspected (NULL = all). '
    'Return columns: phase (before|after|updated), content_type, cnt.';

-- =============================================================================
-- §4  Schema: evidence_count column on agent_lesson           (R2 — consolidate)
-- =============================================================================

ALTER TABLE pgmnemo.agent_lesson
    ADD COLUMN IF NOT EXISTS evidence_count INT NOT NULL DEFAULT 1
        CONSTRAINT ck_agent_lesson_evidence_count CHECK (evidence_count >= 1);

COMMENT ON COLUMN pgmnemo.agent_lesson.evidence_count IS
    'Number of near-duplicate lessons collapsed into this canonical. '
    'Default 1 (standalone lesson). '
    'Set to cluster size by pgmnemo.consolidate() when p_dry_run=FALSE. '
    'Added in v0.14.0.';

-- =============================================================================
-- §5  Function: pgmnemo.consolidate()                         (R2 — consolidate)
--
-- Clusters near-duplicate ACTIVE lessons within the same role using cosine
-- similarity of their embeddings.  Elects a canonical member per cluster
-- (rank: recall_count DESC, confidence DESC, importance DESC, created_at ASC).
--
-- Dry-run (p_dry_run=TRUE, the default):
--   Returns proposed clusters as (canonical_id, member_ids, size, mean_similarity)
--   without performing any writes.
--
-- Apply (p_dry_run=FALSE, inside one transaction):
--   - canonical.evidence_count = cluster size
--   - non-canonical members: state='superseded', is_active=FALSE
--   - SUPERSEDED_BY semantic edge inserted via add_edge() for each member
--     pointing to the canonical
--
-- Guards:
--   - never supersedes a lesson into itself  (CONTINUE WHEN _mbr = canonical)
--   - never collapses across different roles  (pairs restricted to a.role = b.role)
--   - terminal/inactive lessons excluded from candidate pool
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.consolidate(
    p_similarity REAL    DEFAULT 0.92,
    p_dry_run    BOOLEAN DEFAULT TRUE,
    p_role       TEXT    DEFAULT NULL,
    p_limit      INT     DEFAULT 100
)
RETURNS TABLE (
    canonical_id    BIGINT,
    member_ids      BIGINT[],
    size            INT,
    mean_similarity REAL
)
LANGUAGE plpgsql
AS $$
DECLARE
    _pair     RECORD;
    _cluster  RECORD;
    _root_a   BIGINT;
    _root_b   BIGINT;
    _new_root BIGINT;
    _old_root BIGINT;
    _mbr      BIGINT;
BEGIN
    -- Discard any temp tables from a prior call in this session.
    DROP TABLE IF EXISTS _con_candidates;
    DROP TABLE IF EXISTS _con_pairs;
    DROP TABLE IF EXISTS _con_uf;
    DROP TABLE IF EXISTS _con_clusters;

    -- ── 1. Candidate pool ─────────────────────────────────────────────────
    -- Active, non-terminal lessons with embeddings, ordered by canonical rank.
    -- p_limit * 20 is the scan window; output is capped at p_limit clusters.
    CREATE TEMP TABLE _con_candidates ON COMMIT DROP AS
    SELECT id, role, recall_count, confidence, importance, created_at, embedding
    FROM pgmnemo.agent_lesson
    WHERE is_active
      AND state NOT IN ('superseded', 'archived', 'rejected')
      AND embedding IS NOT NULL
      AND (p_role IS NULL OR role = p_role)
    ORDER BY recall_count DESC, confidence DESC, importance DESC, created_at ASC
    LIMIT p_limit * 20;

    -- ── 2. All-pairs cosine similarity above threshold ────────────────────
    -- b.id > a.id avoids duplicate pairs; a.role = b.role enforces role boundary.
    CREATE TEMP TABLE _con_pairs ON COMMIT DROP AS
    SELECT
        a.id                                         AS id_a,
        b.id                                         AS id_b,
        (1.0 - (a.embedding <=> b.embedding))::REAL  AS sim
    FROM _con_candidates a
    JOIN _con_candidates b ON b.id > a.id
                          AND a.role = b.role
    WHERE (1.0 - (a.embedding <=> b.embedding)) >= p_similarity;

    -- ── 3. Union-Find: initialise each paired node as its own root ────────
    CREATE TEMP TABLE _con_uf (id BIGINT PRIMARY KEY, root BIGINT NOT NULL)
    ON COMMIT DROP;

    INSERT INTO _con_uf (id, root)
    SELECT DISTINCT id_a, id_a FROM _con_pairs
    UNION
    SELECT DISTINCT id_b, id_b FROM _con_pairs;

    -- ── 4. Union-Find: unite connected components ─────────────────────────
    FOR _pair IN SELECT id_a, id_b FROM _con_pairs LOOP
        SELECT root INTO _root_a FROM _con_uf WHERE id = _pair.id_a;
        SELECT root INTO _root_b FROM _con_uf WHERE id = _pair.id_b;
        IF _root_a IS DISTINCT FROM _root_b THEN
            _new_root := LEAST(_root_a, _root_b);
            _old_root := GREATEST(_root_a, _root_b);
            UPDATE _con_uf SET root = _new_root WHERE root = _old_root;
        END IF;
    END LOOP;

    -- ── 5. Path compression (flatten transitive chains) ───────────────────
    LOOP
        UPDATE _con_uf u
        SET root = p.root
        FROM _con_uf p
        WHERE p.id = u.root AND p.root <> u.root;
        EXIT WHEN NOT FOUND;
    END LOOP;

    -- ── 6. Materialise cluster results ────────────────────────────────────
    -- Canonical = members[1] (array ordered by recall_count DESC, …).
    -- Only clusters with size > 1 are returned.
    -- Mean similarity = avg of all intra-cluster pairs.
    CREATE TEMP TABLE _con_clusters ON COMMIT DROP AS
    WITH cluster_agg AS (
        SELECT
            u.root,
            array_agg(u.id ORDER BY
                c.recall_count DESC,
                c.confidence   DESC,
                c.importance   DESC,
                c.created_at   ASC,
                c.id           ASC   -- stable tiebreaker: lowest id wins on tie (P1-4)
            )            AS members,
            count(*)::INT AS sz
        FROM _con_uf u
        JOIN _con_candidates c ON c.id = u.id
        GROUP BY u.root
        HAVING count(*) > 1
    ),
    cluster_sim AS (
        SELECT
            ca.root,
            ca.members,
            ca.sz,
            (SELECT avg(p.sim)::REAL
             FROM _con_pairs p
             WHERE p.id_a = ANY(ca.members)
               AND p.id_b = ANY(ca.members)
            ) AS mean_sim
        FROM cluster_agg ca
    )
    SELECT
        members[1]                       AS canonical_id,
        members                          AS all_members,
        sz,
        coalesce(mean_sim, p_similarity) AS mean_sim
    FROM cluster_sim
    ORDER BY sz DESC, members[1]
    LIMIT p_limit;

    -- ── 7. Apply (p_dry_run=FALSE only) — all writes in the caller's tx ───
    IF NOT p_dry_run THEN
        FOR _cluster IN SELECT * FROM _con_clusters LOOP

            -- Stamp canonical with consolidated evidence count
            UPDATE pgmnemo.agent_lesson
            SET evidence_count = _cluster.sz
            WHERE id = _cluster.canonical_id;

            -- Supersede every non-canonical member
            FOREACH _mbr IN ARRAY _cluster.all_members LOOP
                CONTINUE WHEN _mbr = _cluster.canonical_id;  -- self-guard

                -- Direct state update using existing 'superseded' lifecycle value
                UPDATE pgmnemo.agent_lesson
                SET state            = 'superseded',
                    is_active        = FALSE,
                    state_changed_at = NOW()
                WHERE id = _mbr;

                -- Semantic edge: member →[SUPERSEDED_BY]→ canonical
                -- add_edge() maps SUPERSEDED_BY to edge_kind='semantic' (catch-all)
                PERFORM pgmnemo.add_edge(
                    _mbr,
                    _cluster.canonical_id,
                    'SUPERSEDED_BY',
                    _cluster.mean_sim::FLOAT8,
                    jsonb_build_object(
                        'consolidation', TRUE,
                        'cluster_size',  _cluster.sz
                    ),
                    'max'
                );
            END LOOP;
        END LOOP;
    END IF;

    -- ── 8. Return cluster summary (both dry-run and apply paths) ──────────
    RETURN QUERY
    SELECT c.canonical_id, c.all_members, c.sz, c.mean_sim
    FROM _con_clusters c
    ORDER BY c.sz DESC, c.canonical_id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.consolidate(REAL, BOOLEAN, TEXT, INT) IS
    'Cluster near-duplicate ACTIVE lessons by cosine similarity and collapse them. '
    'p_similarity REAL DEFAULT 0.92: cosine similarity threshold for grouping. '
    'p_dry_run BOOLEAN DEFAULT TRUE: when TRUE, return proposed clusters without any writes. '
    'p_role TEXT DEFAULT NULL: restrict to a single role (NULL = all roles). '
    'p_limit INT DEFAULT 100: max clusters to return / process per call. '
    'Returns: (canonical_id BIGINT, member_ids BIGINT[], size INT, mean_similarity REAL). '
    'Algorithm: union-find over all-pairs cosine similarity within each role. '
    'Canonical election: recall_count DESC, confidence DESC, importance DESC, created_at ASC, id ASC (stable tiebreaker). '
    'Apply (p_dry_run=FALSE): canonical.evidence_count=cluster_size; '
    'non-canonical members: state=superseded, is_active=FALSE, state_changed_at=NOW(); '
    'SUPERSEDED_BY edge (semantic) via add_edge() from each member to canonical. '
    'All writes in the caller''s transaction — roll back on error. '
    'Guards: never self-supersedes; never collapses across different roles. '
    'Added in v0.14.0.';
-- End of pgmnemo--0.13.0--0.14.0.sql

-- =============================================================================
-- P1-A  consolidate() — v0.14.1: prior_state recorded in edge metadata
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.consolidate(
    p_similarity REAL    DEFAULT 0.92,
    p_dry_run    BOOLEAN DEFAULT TRUE,
    p_role       TEXT    DEFAULT NULL,
    p_limit      INT     DEFAULT 100
)
RETURNS TABLE (
    canonical_id    BIGINT,
    member_ids      BIGINT[],
    size            INT,
    mean_similarity REAL
)
LANGUAGE plpgsql
AS $$
DECLARE
    _pair        RECORD;
    _cluster     RECORD;
    _root_a      BIGINT;
    _root_b      BIGINT;
    _new_root    BIGINT;
    _old_root    BIGINT;
    _mbr         BIGINT;
    _prior_state TEXT;
BEGIN
    DROP TABLE IF EXISTS _con_candidates;
    DROP TABLE IF EXISTS _con_pairs;
    DROP TABLE IF EXISTS _con_uf;
    DROP TABLE IF EXISTS _con_clusters;

    CREATE TEMP TABLE _con_candidates ON COMMIT DROP AS
    SELECT id, role, recall_count, confidence, importance, created_at, embedding
    FROM pgmnemo.agent_lesson
    WHERE is_active
      AND state NOT IN ('superseded', 'archived', 'rejected')
      AND embedding IS NOT NULL
      AND (p_role IS NULL OR role = p_role)
    ORDER BY recall_count DESC, confidence DESC, importance DESC, created_at ASC
    LIMIT p_limit * 20;

    CREATE TEMP TABLE _con_pairs ON COMMIT DROP AS
    SELECT
        a.id                                         AS id_a,
        b.id                                         AS id_b,
        (1.0 - (a.embedding <=> b.embedding))::REAL  AS sim
    FROM _con_candidates a
    JOIN _con_candidates b ON b.id > a.id
                          AND a.role = b.role
    WHERE (1.0 - (a.embedding <=> b.embedding)) >= p_similarity;

    CREATE TEMP TABLE _con_uf (id BIGINT PRIMARY KEY, root BIGINT NOT NULL)
    ON COMMIT DROP;

    INSERT INTO _con_uf (id, root)
    SELECT DISTINCT id_a, id_a FROM _con_pairs
    UNION
    SELECT DISTINCT id_b, id_b FROM _con_pairs;

    FOR _pair IN SELECT id_a, id_b FROM _con_pairs LOOP
        SELECT root INTO _root_a FROM _con_uf WHERE id = _pair.id_a;
        SELECT root INTO _root_b FROM _con_uf WHERE id = _pair.id_b;
        IF _root_a IS DISTINCT FROM _root_b THEN
            _new_root := LEAST(_root_a, _root_b);
            _old_root := GREATEST(_root_a, _root_b);
            UPDATE _con_uf SET root = _new_root WHERE root = _old_root;
        END IF;
    END LOOP;

    LOOP
        UPDATE _con_uf u
        SET root = p.root
        FROM _con_uf p
        WHERE p.id = u.root AND p.root <> u.root;
        EXIT WHEN NOT FOUND;
    END LOOP;

    CREATE TEMP TABLE _con_clusters ON COMMIT DROP AS
    WITH cluster_agg AS (
        SELECT
            u.root,
            array_agg(u.id ORDER BY
                c.recall_count DESC,
                c.confidence   DESC,
                c.importance   DESC,
                c.created_at   ASC,
                c.id           ASC
            )            AS members,
            count(*)::INT AS sz
        FROM _con_uf u
        JOIN _con_candidates c ON c.id = u.id
        GROUP BY u.root
        HAVING count(*) > 1
    ),
    cluster_sim AS (
        SELECT
            ca.root,
            ca.members,
            ca.sz,
            (SELECT avg(p.sim)::REAL
             FROM _con_pairs p
             WHERE p.id_a = ANY(ca.members)
               AND p.id_b = ANY(ca.members)
            ) AS mean_sim
        FROM cluster_agg ca
    )
    SELECT
        members[1]                       AS canonical_id,
        members                          AS all_members,
        sz,
        coalesce(mean_sim, p_similarity) AS mean_sim
    FROM cluster_sim
    ORDER BY sz DESC, members[1]
    LIMIT p_limit;

    IF NOT p_dry_run THEN
        FOR _cluster IN SELECT * FROM _con_clusters LOOP

            UPDATE pgmnemo.agent_lesson
            SET evidence_count = _cluster.sz
            WHERE id = _cluster.canonical_id;

            FOREACH _mbr IN ARRAY _cluster.all_members LOOP
                CONTINUE WHEN _mbr = _cluster.canonical_id;

                SELECT state INTO _prior_state
                FROM pgmnemo.agent_lesson WHERE id = _mbr;

                UPDATE pgmnemo.agent_lesson
                SET state            = 'superseded',
                    is_active        = FALSE,
                    state_changed_at = NOW()
                WHERE id = _mbr;

                PERFORM pgmnemo.add_edge(
                    _mbr,
                    _cluster.canonical_id,
                    'SUPERSEDED_BY',
                    _cluster.mean_sim::FLOAT8,
                    jsonb_build_object(
                        'consolidation', TRUE,
                        'cluster_size',  _cluster.sz,
                        'prior_state',   _prior_state
                    ),
                    'max'
                );
            END LOOP;
        END LOOP;
    END IF;

    RETURN QUERY
    SELECT c.canonical_id, c.all_members, c.sz, c.mean_sim
    FROM _con_clusters c
    ORDER BY c.sz DESC, c.canonical_id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.consolidate(REAL, BOOLEAN, TEXT, INT) IS
    'Cluster near-duplicate ACTIVE lessons by cosine similarity and collapse them. '
    'p_similarity REAL DEFAULT 0.92: cosine similarity threshold for grouping. '
    'p_dry_run BOOLEAN DEFAULT TRUE: when TRUE, return proposed clusters without any writes. '
    'p_role TEXT DEFAULT NULL: restrict to a single role (NULL = all roles). '
    'p_limit INT DEFAULT 100: max clusters to return / process per call. '
    'Returns: (canonical_id BIGINT, member_ids BIGINT[], size INT, mean_similarity REAL). '
    'Algorithm: union-find over all-pairs cosine similarity within each role. '
    'Canonical election: recall_count DESC, confidence DESC, importance DESC, '
    'created_at ASC, id ASC (stable tiebreaker — P1-4 v0.14.1 fix). '
    'Apply (p_dry_run=FALSE): canonical.evidence_count=cluster_size; '
    'non-canonical members: state=superseded, is_active=FALSE, state_changed_at=NOW(); '
    'SUPERSEDED_BY edge (semantic) via add_edge() from each member to canonical; '
    'edge metadata includes prior_state for undo_consolidate() (v0.14.1 P1-A). '
    'All writes in the caller''s transaction — roll back on error. '
    'Guards: never self-supersedes; never collapses across different roles. '
    'Added in v0.14.0; tiebreaker fix v0.14.1 P1-4; prior_state recording v0.14.1 P1-A.';


-- =============================================================================
-- P1-A  pgmnemo.undo_consolidate() — exact inverse of consolidate() apply
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.undo_consolidate(
    p_canonical_id  BIGINT  DEFAULT NULL,
    p_dry_run       BOOLEAN DEFAULT TRUE
)
RETURNS TABLE (
    canonical_id    BIGINT,
    restored_ids    BIGINT[],
    restored_count  INT
)
LANGUAGE plpgsql
AS $$
DECLARE
    _cluster RECORD;
BEGIN
    FOR _cluster IN
        SELECT
            me.target_id                                                AS cid,
            array_agg(me.source_id          ORDER BY me.source_id)     AS member_ids,
            array_agg(
                COALESCE(me.metadata->>'prior_state', 'candidate')
                ORDER BY me.source_id
            )                                                           AS prior_states,
            count(*)::INT                                               AS sz,
            array_agg(me.id                 ORDER BY me.source_id)     AS edge_ids
        FROM pgmnemo.mem_edge me
        WHERE me.relation_type = 'SUPERSEDED_BY'
          AND me.valid_until   IS NULL
          AND me.metadata @> '{"consolidation":true}'::jsonb
          AND (p_canonical_id IS NULL OR me.target_id = p_canonical_id)
        GROUP BY me.target_id
    LOOP
        IF NOT p_dry_run THEN
            UPDATE pgmnemo.agent_lesson al
            SET state            = u.prior_state,
                is_active        = TRUE,
                state_changed_at = NOW()
            FROM (
                SELECT mid, prior_state
                FROM unnest(_cluster.member_ids, _cluster.prior_states) AS t(mid, prior_state)
            ) u
            WHERE al.id    = u.mid
              AND al.state = 'superseded';

            DELETE FROM pgmnemo.mem_edge
            WHERE id = ANY(_cluster.edge_ids);

            UPDATE pgmnemo.agent_lesson
            SET evidence_count = 1
            WHERE id = _cluster.cid;
        END IF;

        canonical_id    := _cluster.cid;
        restored_ids    := _cluster.member_ids;
        restored_count  := _cluster.sz;
        RETURN NEXT;
    END LOOP;
END;
$$;

COMMENT ON FUNCTION pgmnemo.undo_consolidate(BIGINT, BOOLEAN) IS
    'Exact inverse of pgmnemo.consolidate() apply path (v0.14.1 P1-A). '
    'p_canonical_id BIGINT DEFAULT NULL: NULL=undo all clusters; value=undo that canonical only. '
    'p_dry_run BOOLEAN DEFAULT TRUE: when TRUE, returns planned restoration without any writes. '
    'Returns: (canonical_id BIGINT, restored_ids BIGINT[], restored_count INT). '
    'Per-cluster: restores member state from SUPERSEDED_BY edge metadata field prior_state '
    '(recorded by consolidate() v0.14.1+; falls back to ''candidate'' for pre-0.14.1 edges — '
    'pre-0.14.1 consolidation edges lack prior_state and cannot be exactly inverted; '
    'members are restored to ''candidate'' as the safest active default); '
    'sets is_active=TRUE; deletes the SUPERSEDED_BY consolidation edges; '
    'resets canonical.evidence_count=1. '
    'Guard: only updates members still in state=''superseded'' — safe to call twice. '
    'All writes in the caller''s transaction — roll back on error.';
-- pgmnemo upgrade: 0.14.1 → 0.14.2
-- SPDX-License-Identifier: Apache-2.0
--
-- 0.14.2 — Curation-honesty: reclassify_corpus() must not clobber types set by
-- remember_* verbs.
--
-- ROOT CAUSE (DATA-DESTRUCTIVE, P1):
--   classify_content_type() produces only: procedure | incident | decision | fact | entity.
--   reclassify_corpus() previously touched EVERY active row, including rows whose
--   content_type was set deliberately by a typed write verb.  remember_event() writes
--   content_type='event'; the classifier does not know that type, so reclassify rewrote
--   those rows into classifier categories.  Measured on a live corpus (dry-run): event 10→0.
--
-- FIX:
--   §1  classifier_owned_types() — canonical, single source of truth for the classifier's
--       output domain.  Returns the 5 types the classifier can produce.  reclassify_corpus()
--       derives its candidate filter from this set rather than a deny-list of one value,
--       so any future type written by remember_* verbs is safe by construction.
--   §2  reclassify_corpus() — restrict candidates to rows whose content_type IS NULL or
--       IS IN classifier_owned_types().  All other rows (e.g. 'event', 'relation') are
--       left untouched in both dry-run and live mode.
--   §3  ingest() auto-classify path — already safe (COALESCE: caller value wins over
--       classifier); no change needed.  Documented here for traceability.
--
-- The upgrade script writes only the corrected functions; it does not rebuild the
-- full install.  Sibling tasks may APPEND further fixes to this file.
-- =============================================================================


-- =============================================================================
-- §1  classifier_owned_types() — derive protection set from classifier output domain
-- =============================================================================
-- Returns the exact set of content_type values that classify_content_type() can emit.
-- Rows whose current content_type is NOT in this set and NOT NULL were set by a
-- curator (remember_event, remember_relation, or a future typed verb) and must not
-- be reclassified.
--
-- IMMUTABLE: result is a compile-time constant; value changes only when the
-- classify_content_type() CASE arms change — in which case this function MUST be
-- updated in the same upgrade script.

CREATE OR REPLACE FUNCTION pgmnemo.classifier_owned_types()
RETURNS text[]
LANGUAGE sql
IMMUTABLE PARALLEL SAFE
AS $func$
    SELECT ARRAY['incident', 'decision', 'entity', 'fact', 'procedure']::text[]
$func$;

COMMENT ON FUNCTION pgmnemo.classifier_owned_types() IS
    'Returns the output domain of classify_content_type(): the 5 types the classifier '
    'can produce (incident, decision, entity, fact, procedure). '
    'reclassify_corpus() uses this to derive its candidate filter — rows with '
    'content_type NOT in this set and NOT NULL are curator-owned and must not be '
    'reclassified. Update this function whenever classify_content_type() gains or '
    'loses a CASE arm. v0.14.2.';


-- =============================================================================
-- §2  reclassify_corpus() — restrict candidates to classifier-owned types only
-- =============================================================================
-- Only touch rows whose CURRENT content_type IS NULL (not yet classified) or
-- IS IN pgmnemo.classifier_owned_types() (previously classified by the same
-- classifier and eligible for update).
--
-- Rows with any other content_type — 'event' written by remember_event(),
-- 'relation' written by remember_relation(), or any future curator type — are
-- excluded from candidates entirely and are therefore unchanged in both dry-run
-- and live mode.

CREATE OR REPLACE FUNCTION pgmnemo.reclassify_corpus(
    p_dry_run  boolean DEFAULT true,
    p_limit    int     DEFAULT NULL
)
RETURNS TABLE (phase text, content_type text, cnt bigint)
LANGUAGE plpgsql AS $func$
DECLARE
    _owned text[] := pgmnemo.classifier_owned_types();
BEGIN
    IF p_dry_run THEN
        -- ── DRY-RUN: compute distributions, no writes ──────────────────────
        -- candidates: only rows whose current type is NULL or classifier-owned.
        -- Curator-owned rows (e.g. 'event', 'relation') are excluded.
        RETURN QUERY
        WITH candidates AS (
            SELECT
                al.content_type                                AS old_ct,
                pgmnemo.classify_content_type(al.lesson_text) AS new_ct
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND (al.content_type IS NULL
                   OR al.content_type = ANY(_owned))
            ORDER BY al.id
            LIMIT p_limit
        ),
        -- BEFORE: current distribution of candidate rows only
        before_dist AS (
            SELECT 'before'::text AS ph, c.old_ct AS ct, COUNT(*)::bigint AS n
            FROM candidates c
            GROUP BY c.old_ct
        ),
        -- AFTER: proposed distribution if the live run were applied
        -- COALESCE: when classifier returns NULL, the existing label is kept
        after_dist AS (
            SELECT 'after'::text AS ph,
                   COALESCE(c.new_ct, c.old_ct) AS ct,
                   COUNT(*)::bigint AS n
            FROM candidates c
            GROUP BY COALESCE(c.new_ct, c.old_ct)
        )
        SELECT bd.ph, bd.ct, bd.n FROM before_dist bd
        UNION ALL
        SELECT ad.ph, ad.ct, ad.n FROM after_dist ad
        ORDER BY 1, 3 DESC;

    ELSE
        -- ── LIVE RUN: UPDATE rows with a new (different) classifier label ───
        -- Same candidate filter: skip curator-owned types.
        RETURN QUERY
        WITH to_update AS (
            SELECT al.id,
                   pgmnemo.classify_content_type(al.lesson_text) AS new_ct
            FROM pgmnemo.agent_lesson al
            WHERE al.is_active
              AND (al.content_type IS NULL
                   OR al.content_type = ANY(_owned))
            ORDER BY al.id
            LIMIT p_limit
        ),
        changed AS (
            UPDATE pgmnemo.agent_lesson al
            SET content_type = tu.new_ct,
                updated_at   = NOW()
            FROM to_update tu
            WHERE al.id = tu.id
              AND tu.new_ct IS NOT NULL
              AND (al.content_type IS DISTINCT FROM tu.new_ct)
            RETURNING tu.new_ct AS final_ct
        )
        SELECT 'updated'::text,
               ch.final_ct,
               COUNT(*)::bigint
        FROM changed ch
        GROUP BY ch.final_ct
        ORDER BY 3 DESC;
    END IF;
END;
$func$;

COMMENT ON FUNCTION pgmnemo.reclassify_corpus(boolean, int) IS
    'Corpus self-maintenance: re-derive content_type for active lessons (v0.14.2). '
    'p_dry_run=true (default): returns before/after distributions without writing. '
    'p_dry_run=false: UPDATEs rows where classify_content_type() returns a non-NULL new label. '
    'CURATION-HONESTY (v0.14.2): only touches rows whose content_type IS NULL or belongs to '
    'pgmnemo.classifier_owned_types() — rows set by remember_event(), remember_relation(), '
    'or any future curator verb are excluded from candidates and are left byte-identical. '
    'Protection is derived from the classifier output domain, not a hardcoded deny-list.';


-- =============================================================================
-- §3  ingest() auto-classify — verification note (no code change required)
-- =============================================================================
-- ingest() resolves effective content_type as:
--   _effective_content_type := COALESCE(p_content_type, classify_content_type(p_lesson_text))
-- This means:
--   • When caller passes p_content_type (e.g. remember_event passes 'event'), it wins.
--   • When p_content_type IS NULL, the classifier result fills in.
-- The COALESCE order guarantees a caller-supplied type is NEVER overridden.
-- Verified against 0.14.1 source — no change needed for this fix.


-- =============================================================================
-- P2-A  consolidate() — accumulate evidence_count across multiple runs
-- =============================================================================
-- ROOT CAUSE: evidence_count was SET (overwritten) with the cluster size on each
-- consolidate() call, so after a second run the count reflected only the most recent
-- cluster size, not the running total of near-duplicates ever collapsed.
--
-- FIX: switch from SET to += (accumulate):
--   evidence_count = evidence_count + (cluster_size - 1)
--
-- RATIONALE (preserved here for traceability):
-- After the first consolidation the collapsed members transition to state='superseded'
-- with is_active=FALSE.  The candidate query in consolidate() excludes inactive rows,
-- so a second run can NEVER re-collect already-superseded members.  Accumulation
-- therefore cannot double-count; it records the running total of all near-duplicates
-- ever collapsed into this canonical across all consolidate() passes.
--
-- Evidence_count semantics: "number of near-duplicate originals collapsed into this
-- canonical, not counting the canonical itself" — starts at 1 (standalone) and grows
-- by (cluster_size - 1) per consolidate() pass.
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.consolidate(
    p_similarity REAL    DEFAULT 0.92,
    p_dry_run    BOOLEAN DEFAULT TRUE,
    p_role       TEXT    DEFAULT NULL,
    p_limit      INT     DEFAULT 100
)
RETURNS TABLE (
    canonical_id    BIGINT,
    member_ids      BIGINT[],
    size            INT,
    mean_similarity REAL
)
LANGUAGE plpgsql
AS $$
DECLARE
    _pair        RECORD;
    _cluster     RECORD;
    _root_a      BIGINT;
    _root_b      BIGINT;
    _new_root    BIGINT;
    _old_root    BIGINT;
    _mbr         BIGINT;
    _prior_state TEXT;           -- v0.14.1 P1-A: captured before superseding for undo
BEGIN
    DROP TABLE IF EXISTS _con_candidates;
    DROP TABLE IF EXISTS _con_pairs;
    DROP TABLE IF EXISTS _con_uf;
    DROP TABLE IF EXISTS _con_clusters;

    CREATE TEMP TABLE _con_candidates ON COMMIT DROP AS
    SELECT id, role, recall_count, confidence, importance, created_at, embedding
    FROM pgmnemo.agent_lesson
    WHERE is_active
      AND state NOT IN ('superseded', 'archived', 'rejected')
      AND embedding IS NOT NULL
      AND (p_role IS NULL OR role = p_role)
    ORDER BY recall_count DESC, confidence DESC, importance DESC, created_at ASC
    LIMIT p_limit * 20;

    CREATE TEMP TABLE _con_pairs ON COMMIT DROP AS
    SELECT
        a.id                                         AS id_a,
        b.id                                         AS id_b,
        (1.0 - (a.embedding <=> b.embedding))::REAL  AS sim
    FROM _con_candidates a
    JOIN _con_candidates b ON b.id > a.id
                          AND a.role = b.role
    WHERE (1.0 - (a.embedding <=> b.embedding)) >= p_similarity;

    CREATE TEMP TABLE _con_uf (id BIGINT PRIMARY KEY, root BIGINT NOT NULL)
    ON COMMIT DROP;

    INSERT INTO _con_uf (id, root)
    SELECT DISTINCT id_a, id_a FROM _con_pairs
    UNION
    SELECT DISTINCT id_b, id_b FROM _con_pairs;

    FOR _pair IN SELECT id_a, id_b FROM _con_pairs LOOP
        SELECT root INTO _root_a FROM _con_uf WHERE id = _pair.id_a;
        SELECT root INTO _root_b FROM _con_uf WHERE id = _pair.id_b;
        IF _root_a IS DISTINCT FROM _root_b THEN
            _new_root := LEAST(_root_a, _root_b);
            _old_root := GREATEST(_root_a, _root_b);
            UPDATE _con_uf SET root = _new_root WHERE root = _old_root;
        END IF;
    END LOOP;

    LOOP
        UPDATE _con_uf u
        SET root = p.root
        FROM _con_uf p
        WHERE p.id = u.root AND p.root <> u.root;
        EXIT WHEN NOT FOUND;
    END LOOP;

    CREATE TEMP TABLE _con_clusters ON COMMIT DROP AS
    WITH cluster_agg AS (
        SELECT
            u.root,
            array_agg(u.id ORDER BY
                c.recall_count DESC,
                c.confidence   DESC,
                c.importance   DESC,
                c.created_at   ASC,
                c.id           ASC
            )            AS members,
            count(*)::INT AS sz
        FROM _con_uf u
        JOIN _con_candidates c ON c.id = u.id
        GROUP BY u.root
        HAVING count(*) > 1
    ),
    cluster_sim AS (
        SELECT
            ca.root,
            ca.members,
            ca.sz,
            (SELECT avg(p.sim)::REAL
             FROM _con_pairs p
             WHERE p.id_a = ANY(ca.members)
               AND p.id_b = ANY(ca.members)
            ) AS mean_sim
        FROM cluster_agg ca
    )
    SELECT
        members[1]                       AS canonical_id,
        members                          AS all_members,
        sz,
        coalesce(mean_sim, p_similarity) AS mean_sim
    FROM cluster_sim
    ORDER BY sz DESC, members[1]
    LIMIT p_limit;

    IF NOT p_dry_run THEN
        FOR _cluster IN SELECT * FROM _con_clusters LOOP

            -- P2-A: ACCUMULATE rather than SET — superseded members are excluded
            -- from future scans (is_active=FALSE), so they cannot be re-collected;
            -- accumulation cannot double-count across multiple consolidate() passes.
            UPDATE pgmnemo.agent_lesson
            SET evidence_count = evidence_count + (_cluster.sz - 1)
            WHERE id = _cluster.canonical_id;

            FOREACH _mbr IN ARRAY _cluster.all_members LOOP
                CONTINUE WHEN _mbr = _cluster.canonical_id;

                -- v0.14.1 P1-A: read prior state before superseding
                SELECT state INTO _prior_state
                FROM pgmnemo.agent_lesson WHERE id = _mbr;

                UPDATE pgmnemo.agent_lesson
                SET state            = 'superseded',
                    is_active        = FALSE,
                    state_changed_at = NOW()
                WHERE id = _mbr;

                PERFORM pgmnemo.add_edge(
                    _mbr,
                    _cluster.canonical_id,
                    'SUPERSEDED_BY',
                    _cluster.mean_sim::FLOAT8,
                    jsonb_build_object(
                        'consolidation', TRUE,
                        'cluster_size',  _cluster.sz,
                        'prior_state',   _prior_state   -- v0.14.1 P1-A: for undo_consolidate()
                    ),
                    'max'
                );
            END LOOP;
        END LOOP;
    END IF;

    RETURN QUERY
    SELECT c.canonical_id, c.all_members, c.sz, c.mean_sim
    FROM _con_clusters c
    ORDER BY c.sz DESC, c.canonical_id;
END;
$$;

COMMENT ON FUNCTION pgmnemo.consolidate(REAL, BOOLEAN, TEXT, INT) IS
    'Cluster near-duplicate ACTIVE lessons by cosine similarity and collapse them. '
    'p_similarity REAL DEFAULT 0.92: cosine similarity threshold for grouping. '
    'p_dry_run BOOLEAN DEFAULT TRUE: when TRUE, return proposed clusters without any writes. '
    'p_role TEXT DEFAULT NULL: restrict to a single role (NULL = all roles). '
    'p_limit INT DEFAULT 100: max clusters to return / process per call. '
    'Returns: (canonical_id BIGINT, member_ids BIGINT[], size INT, mean_similarity REAL). '
    'Algorithm: union-find over all-pairs cosine similarity within each role. '
    'Canonical election: recall_count DESC, confidence DESC, importance DESC, '
    'created_at ASC, id ASC (stable tiebreaker — P1-4 v0.14.1 fix). '
    'Apply (p_dry_run=FALSE): canonical.evidence_count += (cluster_size - 1) [P2-A]; '
    'non-canonical members: state=superseded, is_active=FALSE, state_changed_at=NOW(); '
    'SUPERSEDED_BY edge (semantic) via add_edge() from each member to canonical; '
    'edge metadata includes prior_state for undo_consolidate() (v0.14.1 P1-A). '
    'All writes in the caller''s transaction — roll back on error. '
    'Guards: never self-supersedes; never collapses across different roles. '
    'Added in v0.14.0; tiebreaker fix v0.14.1 P1-4; prior_state recording v0.14.1 P1-A; '
    'evidence_count accumulation P2-A v0.14.2.';


-- =============================================================================
-- P2-D  classify_content_type() — narrow 'rejected' to prevent context-blind
--       false-positives on operational text
-- =============================================================================
-- ROOT CAUSE: the bare word-boundary pattern \mrejected\M matched any sentence
-- containing "rejected" (e.g. "The PR was rejected for having typos") and
-- classified it as 'decision', even though no decision is being recorded.
--
-- FIX: remove the bare \mrejected\M arm from the decision branch, mirroring the
-- treatment of \mbugfix\M in v0.14.1.  Genuine rejection decisions are still
-- caught by the remaining arms:
--   • 'decided', 'decision', 'verdict', 'approved', 'chosen'
--   • ADR[-\s]?\d pattern
--   • compound selection patterns (chose/selected/adopted + instead/over/rather than)
-- =============================================================================

CREATE OR REPLACE FUNCTION pgmnemo.classify_content_type(p_text text)
RETURNS text
LANGUAGE sql
IMMUTABLE
STRICT
AS $func$
    SELECT CASE

        -- ── INCIDENT (highest priority) ────────────────────────────────────
        WHEN p_text ~* '\m(root.?cause|postmortem|outage|incident|hotfix|regression|rollback|revert)\M'
          OR p_text ~* '\m(stacktrace|traceback|segfault|oom.?kill)\M'
          OR (p_text ~* '\m(crashed?|broken|corrupted?|panicked?|failed)\M'
              AND p_text ~* '\m(caused|because of|due to|led to|resulted?|triggered?)\M')
          OR p_text ~* '\mthe (bug|error|issue|failure) (was|is)\M'
          -- NOTE: \mbugfix\M removed in v0.14.1 — caused false-positives on process
          -- text like "When submitting a bugfix PR, always …" (should be 'procedure').
        THEN 'incident'

        -- ── DECISION ────────────────────────────────────────────────────────
        WHEN p_text ~* '\mADR[-\s]?\d'
          OR p_text ~* '\m(decided|decision|verdict|approved|chosen)\M'
          -- NOTE: \mrejected\M removed in v0.14.2 — caused false-positives on
          -- operational text like "The PR was rejected for having typos" (should
          -- be NULL, not 'decision').  True rejection decisions fire via 'decided',
          -- 'decision', 'verdict', 'approved', 'chosen', or the compound patterns.
          OR p_text ~* 'we (chose|selected|adopted|agreed|preferred?|decided)\M'
          OR (p_text ~* '\m(chose|selected|adopted)\M'
              AND p_text ~* '\m(instead|over|rather than|in favor of)\M')
        THEN 'decision'

        -- ── ENTITY ──────────────────────────────────────────────────────────
        WHEN p_text ~* '\mis (?:a|the)(?:\s+\w+){0,2}\s+(?:service|tool|library|module|package|repo|repository|component|system|project)\M'
          OR p_text ~* '\mis (?:the)(?:\s+\w+){0,1}\s+(?:lead|owner|author|contact|maintainer|engineer|manager|architect|operator|po)\M'
          OR p_text ~* '\m(owns?|maintains?|is responsible for)\M'
        THEN 'entity'

        -- ── FACT ────────────────────────────────────────────────────────────
        WHEN (   p_text ~* '\m(defaults? to|default (value |port |behavior )?is)\M'
              OR p_text ~* '\malways returns?\M'
              OR p_text ~* '\m(is always|is set to|is stored in|is fixed at|is never)\M'
              OR p_text ~* '\mversion (is|was)\M'
              OR p_text ~* '\m(schema|table|column|port|endpoint) (is|has|was|uses?|contains?)\M'
             )
          AND p_text !~* '\mwhen\M'
          AND p_text !~* '\m(run|install|deploy|configure|restart|use)\M'
        THEN 'fact'

        -- ── PROCEDURE (catch-all) ────────────────────────────────────────────
        WHEN p_text ~* '\mwhen\M'
          OR p_text ~* '\m(must|should|always|never|avoid|prefer|recommend)\M'
          OR p_text ~* '\m(run|execute|install|deploy|configure|restart|use)\M'
          OR p_text ~* '\m(command|script|workflow|pattern|approach|method)\M'
          OR p_text ~* '\mhow to\M'
        THEN 'procedure'

        ELSE NULL
    END
$func$;

COMMENT ON FUNCTION pgmnemo.classify_content_type(text) IS
    'Deterministic keyword/regex content-type classifier (v0.14.2). No LLM. '
    'Returns: procedure | incident | decision | fact | entity | NULL. '
    'Priority: incident > decision > entity > fact > procedure > NULL. '
    'IMMUTABLE + STRICT: NULL input → NULL output; result is purely a function of the text. '
    'v0.14.1: removed bare \mbugfix\M arm — caused false-positives on process text. '
    'v0.14.2: removed bare \mrejected\M arm — caused false-positives on operational '
    'text like "The PR was rejected for having typos" (P2-D). '
    'Used by ingest() auto-fill and reclassify_corpus().';
